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Use of adjuvant chemotherapy (AC) and outcomes in stage II colon cancer (CC) with versus without poor prognostic features.

2013· article· en· W2591514632 on OpenAlexaff
Aalok Kumar, Hagen F. Kennecke, Howard J. Lim, Daniel J. Renouf, Ryan Woods, Caroline Speers, Winson Y. Cheung

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicinePerineural invasionInternal medicineLymphovascular invasionStage (stratigraphy)Proportional hazards modelOncologyPerforationCancerGastroenterologySurgeryMetastasis

Abstract

fetched live from OpenAlex

338 Background: AC is frequently considered in patients (pts) with "high risk" stage II CC, defined by the presence of >/=1 poor prognostic features such as obstruction or perforation, T4 stage, <12 lymph nodes removed, positive resection margins, and lymphovascular or perineural invasion. Survival benefits associated with AC use in high risk pts remain largely unproven. Our aims were to examine patterns of AC use in stage II CC and to explore the impact of AC on survival in high vs. low risk pts. Methods: All pts with stage II CC in British Columbia from 1999 to 2008 and evaluated at 1 of 5 regional cancer centers were reviewed. Kaplan-Meier and Cox regression methods were used to correlate high vs. low risk status and receipt of AC with relapse-free (RFS), disease-specific (DSS), and overall survival (OS). Results: We identified 1,697 pts: 1,236 (73%) high risk and 461 (27%) low risk among whom 363 (29%) and 61 (13%) received AC, respectively. Individuals with high risk disease who received AC were younger (median 62 vs. 72 yrs, p<0.01) and had better performance status (ECOG 0/1 47% vs. 34%, p=0.02). For high risk pts, AC was associated with improved 5-year OS (Table). Adjusting for confounders, an OS advantage from AC persisted for high risk pts (HR 0.67, 95CI 0.52-0.86, p=0.002), with no significant RFS or DSS benefits. Subgroup analyses revealed individuals with T4 lesions had significantly improved RFS (HR 0.63, 95CI 0.42-0.95, p=0.03), DSS (HR 0.59, 95CI 0.37-0.93, p=0.03), and OS (HR 0.50, 95CI 0.33-0.77, p=0.002). For low risk pts, AC was associated with worse RFS (HR 2.27, 95CI 1.03-4.97, p=0.04) and DSS (HR 2.97, 95CI 1.10-8.02, p=0.03). Conclusions: In this population-based cohort study, AC was associated with an OS advantage in high risk pts, likely due to pt selection. RFS and DSS benefits were mainly seen in T4 lesions, suggesting a limited role for AC in pts deemed high risk. A possible trend towards harm was seen in the low risk group receiving AC. Better risk stratification schemes including those that incorporate molecular testing are warranted. [Table: see text]

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.125
GPT teacher head0.461
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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