Predictors of treatment interruption/dose reduction of neoadjuvant chemotherapy for rectal cancer: A multicenter study.
Bibliographic record
Abstract
580 Background: Neoadjuvant chemoradiation (CRT) is the standard of care for patients with locally advanced rectal cancer. Many patients require dose reduction or chemotherapy interruption due to significant toxicities. To assess the predictors of neoadjuvant chemotherapy treatment (tx) adjustments, we performed a retrospective 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 CRT and had curative intent surgery (Sx) from 2005 to 2012. Patient, tumor and tx characteristics were correlated with treatment completion. Results: Of the 891 patients included, 886 patients had tx dose adjustments data available. 738 (83.2%) completed the planned neoadjuvant chemotherapy, while 148 (16.7%) failed to complete planned chemotherapy. Patients who required tx interruption/cessation or dose reduction were more likely to be female, elderly, had higher ECOG PS and were treated with fluorouracil (FU) chemotherapy in univariate analysis (see Table). On multivariable analysis, female gender (OR 1.807, 95% CI 1.02-3.2, p=0.042) and tx with FU (vs capecitabine) (OR 2.7, 95% CI 1.52-4.77, p=0.0007) were associated with dose reduction and tx interruption/cessation. Conclusions: Gender and type of chemotherapy are predictors of neoadjuvant chemotherapy interruption or dose reduction in rectal cancer. Careful monitoring of these patients is warranted during neoadjuvant CRT. [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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".