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Record W2791264275 · doi:10.1002/jso.25009

Comparison of two novel staging systems with the TNM system in predicting stage III colon cancer survival

2018· article· en· W2791264275 on OpenAlexaff
Richard Walker, Trevor Wood, Emily Souder, Michelle C. Cleghorn, Manjula Maganti, Andrea J. MacNeill, Fayez A. Quereshy

Bibliographic record

VenueJournal of Surgical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsVancouver General HospitalBC Cancer AgencyPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineConcordanceTNM staging systemStage (stratigraphy)Colorectal cancerLymph nodeStaging systemInternal medicineOncologySurvival analysisOverall survivalCancer

Abstract

fetched live from OpenAlex

Background and Objectives Adaptations of the TNM staging system that incorporate the Lymph Node Ratio (LNR) have been proposed for stage III colon cancer. This study compared the concordance of two novel staging systems and the TNM system with observed survival outcomes in stage III patients. Methods A review of patients who underwent surgery for stage III colon cancer between January 2002 and April 2015 at a tertiary care centre was performed. The Kaplan‐Meier method was used to estimate the 5‐year overall (OS) and disease free survival (DFS) rates, and the concordance probability was calculated to evaluate the discriminatory power of the staging systems. Results Two hundred and sixty‐one patients were identified. For TNM stages IIIA, IIIB, and IIIC, 5‐year OS was 83.4%, 67.6%, and 38.3%, respectively ( P < 0.001). All three staging systems were independently predictive of OS and DFS ( P < 0.001). However, the novel staging system by Sugimoto et al 18 was the most favourable prognostic tool, with a concordance of 0.646 for DFS and 0.659 for OS. Conclusions The novel staging system by Sugimoto et al 18 was superior to the TNM system. Incorporating LNR into staging models for node positive colon cancers may improve survival information available to patients and potentially aid treatment decisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.414
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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