Impact of duration of neoadjuvant radiation on rectal cancer survival.
Bibliographic record
Abstract
682 Background: The utility of neoadjuvant radiation (XRT) for the treatment of stages II-III rectal cancer has been demonstrated previously. However, the optimal amount and duration XRT in this setting remains unknown. Using a population-based cohort of stage II and II rectal cancer (RC) patients treated with curative intent including XRT, our aims were to 1) examine the patterns in XRT use and 2) explore the relationship between XRT course and survival. Methods: We analyzed patients diagnosed with clinical stage II-III RC from 2006 to 2010 and treated with long course 45-50.4 Gray (LC) or short course 25 Gray (SC) XRT at any 1 of 5 regional cancer centers in British Columbia. Logistic regression models were constructed to determine the factors associated with the course of XRT given, LC vs. SC. Kaplan-Meier methods and Cox regression that accounted for known prognostic factors were used to evaluate the relationship between XRT course and disease-free (DFS), overall survival (OS), local recurrence free survival (LRFS) and distant recurrence free survival (DRFS). Results: 427 patients were identified: median age 65 years (range 31 to 94), 67% men, 87% T3/4 tumors, and 74% with N1 or N2 disease. Among them, 240 (56%) received SC and 187 (44%) received LC. Adjusting for confounders, patients with N1 or N2 disease were more likely to receive LC (OR for LC 5.08, 95% CI, 2.51-11.22, p<0.0001 and 8.35, 95% CI, 3.35-22.39, p<0.0001, respectively), while older age patients were less likely to receive LC (OR 0.95, 95% CI, 0.94-0.98, p<0.0001). On univariate analysis, there was no significant difference seen in DFS, OS, LRFS, and DRFS between LC and SC. Similarly, in multivariate analyses comparing LC vs. SC, the course of XRT was not associated with differences in DFS (HR 1.06, 95% CI, 0.68-1.64, p=0.80), OS (HR 0.91, 95% CI, 0.61-1.37, p=0.66), LRFS (HR 0.79, 95% CI, 0.39-1.57, p=0.50) and DRFS (HR 0.99, 95% CI, 0.60-1.61, p=0.95). Additional baseline clinical and tumor characteristics did not influence outcomes (all p>0.05). Conclusions: Appropriate pre-operative selection of SC vs. LC neoadjuvant XRT for early stage RC based on patient and tumor characteristics was not associated with differences in survival outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".