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A population-based analysis of outcomes in cancer patients who do not satisfy clinical trial eligibility criteria (CTEC).

2013· article· en· W2794946998 on OpenAlexaff
Winson Y. Cheung, Hagen F. Kennecke, Howard J. Lim, Daniel J. Renouf, Sharlene Gill, Özge Göktepe, Caroline Speers

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineInternal medicinePopulationCancerMultivariate analysisClinical trialRenal functionSurgery

Abstract

fetched live from OpenAlex

6502 Background: Trials have stringent inclusion and exclusion criteria to maintain internal validity. However, study findings are often applied to patients in routine practice who do not meet CTEC. Our aim was to characterize the outcomes and magnitude of treatment benefit in these patients. Methods: Patients diagnosed with stage III colon cancer from 2006 and 2008, referred to 1 of 5 regional cancer centers in British Columbia, and assessed for adjuvant chemotherapy (AC) within 12 weeks of surgery were analyzed. Patients were considered trial-eligible (TE) if aged 18 to 79 years, ECOG 0/1, CEA <10, did not receive prior chemotherapy or radiation, and had adequate blood counts and normal cardiac, liver and kidney function. All other patients were deemed trial-ineligible (TI). Results: A total of 820 patients were identified: median age was 69 years (range 60-76), 423 (52%) were men, 365 (45%) were ECOG 0/1 and 592 (72%) received AC. Among patients treated with AC, 370 (63%) were TE and 222 (37%) were TI. Compared to TI patients, those who were TE were younger (63 vs 70 years, p<0.01) and more likely to receive combination regimens rather than single agent AC (56 vs 33%, p<0.01). Outcomes were significantly different among patients who were TE, TI, and those who did not receive AC (Table). In multivariate analyses that adjusted for known prognostic factors such as age, ECOG and T and N stages, both TI patients and those not treated with AC had worse outcomes than TE patients (HR for colon cancer death 1.32, 95%CI 0.86-2.02 and 2.77, 95%CI 1.92-3.99, respectively, p trend <0.01; HR for all cause death 1.24, 95%CI 0.85-1.80 and 2.95, 95%CI 2.17-4.00, respectively, p trend <0.01). Conclusions: In this population-based cohort, colon cancer patients who did not fit CTEC were frequently treated with AC. Outcomes in this TI group were inferior to those in the TE group, but they were better than the subset that did not receive AC. Broadening CTEC to include a segment of the TI population should be considered as there appears to be benefit in selected individuals. [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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.177
GPT teacher head0.567
Teacher spread0.391 · 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.

Study designObservational
DomainMethods
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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Citations0
Published2013
Admission routes1
Has abstractyes

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