Concomitant Use of Proton Pump Inhibitors With Capecitabine Based Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer
Bibliographic record
Abstract
AIM: Capecitabine (Cape) is routinely used for the neoadjuvant chemoradiation treatment (NACRT) of locally advanced rectal cancers (LARCs). Previous reports have suggested that the concomitant use of proton pump inhibitors (PPIs) may affect the efficacy of Cape, although the true effect of PPIs when used with Cape as a radiosensitizer for neoadjuvant radiation is unclear. The aim of our study was to evaluate the impact of concurrent PPI use along with fluorouracil (FU) and Cape based NACRT in terms of pathologic and oncological outcomes, in patients with LARC. METHODS: LARC patients treated at our center with NACRT from 2010 to 2016 were identified. Postoperative pathology and follow-up outcomes were examined for any differences with relation to the use of PPIs concurrently with FU and Cape based NACRT and adjuvant chemotherapy regimens. RESULTS: Three hundred four and 204 patients received treatment with FU and Cape based NACRT. No difference in pathologic complete response rate was noted between the 2 arms with the concurrent use of PPIs (25.8% and 25%, respectively, P=0.633); or with and without the use of PPIs in the Cape-NACRT arm specifically (20% and 20.7%, P=0.945). At a median follow-up of 5 years, no statistical difference in local or distant control was noted in the Cape-NACRT patients, with and without concomitant PPI use (P=0.411 and 0.264, respectively).Multivariate analysis showed no association of PPI use and NACRT with Cape, in terms of local control (hazard ratio=0.001, P=0.988) or overall survival (hazard ratio=1.179, confidence interval=0.249-5.579, P=0.835). CONCLUSIONS: Our study revealed that there was no adverse pathologic or oncological outcome with the concurrent use of PPIs along with Cape-NACRT in the treatment of LARC. We report that it may be safe to use PPIs if essential, in this clinical setting, although it would be wise to exercise caution.
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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 | 0.001 |
| 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) | 0.001 | 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".