Predictors of pathologic complete response after neoadjuvant treatment for rectal cancer: A multicenter study.
Bibliographic record
Abstract
397 Background: Pathologic complete response (pCR) to neoadjuvant chemoradiation (CRT) for rectal cancer is associated with better long-term outcomes, and is used as an early indicator of response to novel agents. To assess the rate and predictors of pCR, we performed a retrospective population based study in four Canadian provinces. Methods: Cancer Registries identified consecutive patients with clinical stage I-III rectal cancer from the Tom Baker Cancer Center, Cross Cancer Institute, BC Cancer Agency, Ottawa Hospital Cancer Centre and the Dr. H. Bliss Murphy Cancer Centre who received fluoropyrimidine-based CRT and had curative intent surgery (Sx) from 2005 to 2012. Patient, tumor, and therapy characteristics were correlated with response. Results: Of the 891 patients included, 885 patients had pCR data available. 161 (18.2%) had a pCR to CRT, while 724 (81.8%) did not. Patients with a pCR had a lower pre-treatment (tx) CEA, and higher hemoglobin on univariate analysis (see table). On multivariable analysis, statin use at baseline (OR 1.7, 95% CI 1.04-2.89, p=0.044), lower pre-tx CEA (OR 1.03, 95% CI 1.003-1.05 p=0.028) and distance closer to anal verge (OR 1.07, 95% CI 1.004-1.15, p=0.039) were significant predictors of pCR. The 3yr DFS was 86% in those with pCR vs 62.5% in those without a pCR (P<0.0001). Conclusions: Lower pre-tx CEA, distance closer to anal verge and statin use are predictors of pCR. Clinical trials investigating statins combined with neoadjuvant CRT may be warranted. [Table: see text]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".