Corpus Engine Towards a reproducible .science file.
← All .science files

pmid:37519809

Prognostic value of neutrophil extracellular trap signature in clear cell renal cell carcinoma.

Rong Li · Xuewen Jiang · Pin Wang · Xiaoyan Liu

Frontiers in oncology · 2023 doi:10.3389/fonc.2023.1205713

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:37519809
kind
science-record
content_hash
sha256:ecf3806e229fbad42336619c41c0055b948f013e4dcd62b4778b1e9ec0f0b183
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T19:02:17Z
updated_at
2026-11-01T22:09:58Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Prognostic value of neutrophil extracellular trap signature in clear cell renal cell carcinoma.
doi
10.3389/fonc.2023.1205713
pmid
37519809
authors array · 4 items
[0]
Rong Li
[1]
Xuewen Jiang
[2]
Pin Wang
[3]
Xiaoyan Liu
journal
Frontiers in oncology
year
2023
abstract
Introduction: Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of urological carcinoma. Although targeted therapy and immunotherapy are usually employed, they often result in primary and acquired resistance. There is currently a lack of dependable biomarkers that can accurately anticipate the prognosis of ccRCC. Recent research has indicated the critical role of neutrophil extracellular traps (NETs) in the development, metastasis, and immune evasion of cancer. The aim of this study was to explore the value of NETs in the development and prognosis of ccRCC. Methods: Clinical features and genetic expression information of ccRCC patients were acquired from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. NETs-related gene set were obtained from previous studies. A NETs-related gene signature was constructed based on TCGA data and validated using ICGC and E-MTAB-1980 databases. Furthermore, the immune microenvironment and responsiveness to anticancer medications in ccRCC patients with varying levels of NETs risks were investigated. Results: A total of 31 NET-related genes were differently expressed between normal kidney and ccRCC tissues. 17 out of 31 were significantly associated with overall survival. After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group. This signature demonstrated excellent performance in predicting the overall survival of TCGA patients as well as the validation ICGC and E-MTAB-1980 patients. Additionally, the NETs signature was significantly correlated with immune infiltration and drug sensitivity. Conclusions: The NETs signature established by the current study has prognostic significance in ccRCC, and may serve as a useful biomarker for patient stratification and treatment decisions. Further validation and clinical studies are required to fully translate these findings into clinical practice.
claims array · 3 items
[0] object · 6 fields
id
claim-method
statement
Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator).
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37519809
locator
sections[5] "Construction of a ccRCC prognostic model based on NETs-related genes" paragraph 2
quote
Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator).
state
complete
[1] object · 6 fields
id
claim-data-access
statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37519809
locator
sections[19] "Data availability statement" paragraph 2
quote
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/.
state
complete
[2] object · 6 fields
id
claim-target-result
statement
After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group.
kind
qualitative
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:37519809
locator
sections[0] "Abstract" paragraph 3
quote
After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group.
state
complete
gaps array · 0 items
experiment object · 8 fields
state
partial
objective
After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group.
steps array · 3 items
[0] object · 13 fields
id
read_published_estimate
order
1
name
Read the published estimate from the source
kind
other
state
complete
blocked_on
null
description
The claim, method and evidence span published with the source article.
parameters object · 0 fields
inputs array · 1 item
[0]
source:pmid:37519809
outputs array · 1 item
[0]
the published estimate and its evidence
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
id
obtain_source_data
order
2
name
Obtain the source data from the named repository
kind
data-preparation
state
blocked
blocked_on
data:accession_absent
description
The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
[0]
the source data with its repository provenance
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
id
run_analysis
order
3
name
Run the published analysis
kind
analysis
state
complete
blocked_on
null
description
Apply the paper's statistical method to the resolved series and compare the result with the published target.
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
[0]
the replicated result and its comparison
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 1 item
[0]
step:obtain_source_data blocked on data:accession_absent
data object · 3 fields
state
partial
assets array · 1 item
[0] object · 15 fields
id
source:pmid:37519809
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.3389/fonc.2023.1205713
sha256
sha256:55ada61543111f6729f44660a105dd4d0e59f443412ad4b97e8af9947af3d923
bytes
40395
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.3389/fonc.2023.1205713
version
null
note
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
structure object · 4 fields
pmcid
PMC10374836
source
europe_pmc_fulltext
coverage
whole_body
text_hash_scope
rendered_section_body
gaps array · 0 items
analysis object · 4 fields
state
partial
methods array · 4 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator).
[1] object · 10 fields
id
method-2
family
group_comparison
specific
wilcoxon
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
group_comparison_wilcoxon
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
Wilcoxon
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
kaplan_meier
label
Survival analysis
dsl_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Kaplan-Meier
[3] object · 10 fields
id
method-4
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log-rank
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
blocked
tier
R4
blocked_on
data:accession_absent
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R4
reachable
false
accessions array · 0 items
resolved_accessions array · 0 items
statements array · 1 item
[0]
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/.
basis
The paper names no accession to its source data, so the data has to come from the authors.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 1 item
[0]
source:pmid:37519809
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:None
gaps array · 1 item
[0]
data:accession_absent
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:48:20Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
12102
completion_tokens
2145
cached_tokens
128
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:37519809
type
paper
label
pmid:37519809
[1] object · 3 fields
id
source:pmid:37519809
type
manuscript
label
PMID 37519809 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:37519809
type
paper
label
pmid:37519809
[1] object · 3 fields
id
source:pmid:37519809
type
manuscript
label
PMID 37519809 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-37519809
type
extraction
used array · 1 item
[0]
source:pmid:37519809
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-None
type
accession_resolution
used array · 1 item
[0]
data:geo:None
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:37519809
parent_hash
sha256:8b5bbf0462499d850d02b3e44af96960f32c1d8c0ade409f039c4597464f0de9
fork_of
null
gaps array · 6 items
[0]
execution.not_run
[1]
method.reader_differs_from_extractor
[2]
review.human_critique
[3]
review.independent_reader
[4]
review.machine_critique
[5]
step:obtain_source_data blocked on data:accession_absent
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator).
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37519809", "kind": "science-record", "revision": 3, "content_hash": "sha256:ecf3806e229fbad42336619c41c0055b948f013e4dcd62b4778b1e9ec0f0b183", "created_at": "2026-11-01T19:02:17Z", "updated_at": "2026-11-01T22:09:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic value of neutrophil extracellular trap signature in clear cell renal cell carcinoma.", "doi": "10.3389/fonc.2023.1205713", "pmid": "37519809", "authors": ["Rong Li", "Xuewen Jiang", "Pin Wang", "Xiaoyan Liu"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Introduction: Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of urological carcinoma. Although targeted therapy and immunotherapy are usually employed, they often result in primary and acquired resistance. There is currently a lack of dependable biomarkers that can accurately anticipate the prognosis of ccRCC. Recent research has indicated the critical role of neutrophil extracellular traps (NETs) in the development, metastasis, and immune evasion of cancer. The aim of this study was to explore the value of NETs in the development and prognosis of ccRCC.\nMethods: Clinical features and genetic expression information of ccRCC patients were acquired from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. NETs-related gene set were obtained from previous studies. A NETs-related gene signature was constructed based on TCGA data and validated using ICGC and E-MTAB-1980 databases. Furthermore, the immune microenvironment and responsiveness to anticancer medications in ccRCC patients with varying levels of NETs risks were investigated.\nResults: A total of 31 NET-related genes were differently expressed between normal kidney and ccRCC tissues. 17 out of 31 were significantly associated with overall survival. After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group. This signature demonstrated excellent performance in predicting the overall survival of TCGA patients as well as the validation ICGC and E-MTAB-1980 patients. Additionally, the NETs signature was significantly correlated with immune infiltration and drug sensitivity.\nConclusions: The NETs signature established by the current study has prognostic significance in ccRCC, and may serve as a useful biomarker for patient stratification and treatment decisions. Further validation and clinical studies are required to fully translate these findings into clinical practice.", "claims": [{"id": "claim-method", "statement": "Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37519809", "locator": "sections[5] \"Construction of a ccRCC prognostic model based on NETs-related genes\" paragraph 2", "quote": "Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37519809", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37519809", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37519809"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37519809", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2023.1205713", "sha256": "sha256:55ada61543111f6729f44660a105dd4d0e59f443412ad4b97e8af9947af3d923", "bytes": 40395, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2023.1205713", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10374836", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator)."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The accession number for TCGA is phs000178.v11.p8. And all the data were downloaded from https://xenabrowser.net. The accession number for ICGC is RECA-EU. Relevant data were obtained from https://dcc.icgc.org/."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37519809"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:48:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12102, "completion_tokens": 2145, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37519809", "type": "paper", "label": "pmid:37519809"}, {"id": "source:pmid:37519809", "type": "manuscript", "label": "PMID 37519809 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37519809", "type": "paper", "label": "pmid:37519809"}, {"id": "source:pmid:37519809", "type": "manuscript", "label": "PMID 37519809 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37519809", "type": "extraction", "used": ["source:pmid:37519809"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37519809", "parent_hash": "sha256:8b5bbf0462499d850d02b3e44af96960f32c1d8c0ade409f039c4597464f0de9", "fork_of": null, "generation": 2}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Subsequently, based on the results obtained from the aforementioned analysis, we utilized the “glmnet” R package to develop a prognosis model, utilizing LASSO (least absolute shrinkage and selection operator)."], "order_basis": "analysis_only", "id": "assay:1"}]}