Impact of dose-capping chemotherapy in concurrent chemoradiotherapy in rectal cancer patients
Bibliographic record
Abstract
Introduction The study evaluated the effect of chemotherapy dose-capping on disease recurrence, toxicity and survival of rectal cancer patients treated with chemoradiotherapy (CRT). Methods 601 consecutive rectal cancer patients treated with concurrent CRT were retrospectively analysed. Dose-capped patients were defined as having a body surface area (BSA) ≥2.0 m 2 and who received <95% full weight-based chemotherapy dose. Binary logistic regression was used to study the factors associated with the outcome variables (capped vs. uncapped). Kaplan-Meier estimation evaluated significant predictors of survival. Results The median follow-up time was 7.54 years. The rate of disease recurrence was significantly higher in dose-capped patients (35%) compared to those without dose-capping (24%, P = 0.016). The adjusted odds ratio for dose-capped patients experiencing recurrence was 1.64 compared to uncapped patients (95% CI, 1.10–2.43). Overall, dose-capped patients were less likely to experience significant toxicity requiring dose reduction and/or treatment break when compared to uncapped patients (15% and 28% respectively, P = 0.008).There was significant differences in PFS between capped and uncapped group (77% vs. 85%; P = 0.017). The 5-year OS in the capped group was 75.0%, and 80% in the uncapped group ( P = 0.149). Conclusions Rectal cancer patients treated with dose-capped CRT were at increased risk of disease recurrence. Patients dosed by actual BSA did experience excessive toxicity compared to dose-capped group. We recommend that chemotherapy dose-capping based on BSA should not be practiced in rectal cancer patients undergoing CRT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".