Predictors of pathologic complete response (pCR) after neoadjuvant chemoradiation (Neo CRT) for rectal cancer: A multicenter population-based study.
Bibliographic record
Abstract
e14073 Background: pCR to Neo CRT for rectal cancer is associated with better outcomes and used as an early indicator of response. To assess the rate and predictors of pCR, as well as access to care, we performed a retrospective study in two Canadian provinces. Methods: Cancer registries identified consecutive patients with clinical stage I-III rectal cancer from the Tom Baker Cancer Center, Cross Cancer Institute, and Dr. H. Bliss Murphy Cancer Centre who received Neo CRT and had curative intent surgery (Sx) from 2005 to 2011. Patient, tumor and therapy characteristics were correlated with response. Results: 301 patients were included of which 59 (19.6%) had a pCR to Neo CRT. At a median follow-up of 17 months, disease free survival was 96.7% for pCR vs 82.3% for non-pCR (p=0.005). 43 (73%) patients with pCR received adjuvant chemotherapy including bolus FU 27 (63%), capecitabine 10 (23%) and oxaliplatin-based 6 (14%). Median time from diagnosis to consult was 4 weeks (wks), from consult to start of Neo CRT 3.3 wks and start of CRT to Sx 13 wks. On multivariate analysis a low pre-op CEA (p=0.0323) was a significant independent predictor of pCR while statin use at initial consult (p=0.077) and higher pre-op hemoglobin (p=0.0974) trended toward significance when adjusted for clinical stage. Conclusions: Rates of pCR in a population based setting are substantial. A lower pre-op CEA is associated with a pCR to Neo CRT. Statin use and pre-op hemoglobin require further investigation. Our access to care data provides a baseline for future comparisons. [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 | 0.000 | 0.001 |
| 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".