Statin therapy in patients undergoing short-course neoadjuvant radiotherapy for rectal cancer: A retrospective cohort study
Bibliographic record
Abstract
Abstract Purpose There is a potential benefit with concurrent statin use and neoadjuvant therapy for rectal cancer. The impact of statins on pathologic response following short-course neoadjuvant radiation has yet to be studied. This study aimed to elucidate the impact statin use on tumor response to short-course neoadjuvant radiation. Methods This retrospective cohort study included patients receiving short-course neoadjuvant radiation and subsequently undergoing oncologic resection for stage II/III rectal adenocarcinoma from 2014–2020. Exclusion criteria included recurrent disease, total neoadjuvant therapy (TNT), and oncologic resection less than six weeks after neoadjuvant therapy. The primary outcome was pathologic complete response (pCR). Secondary outcomes included graded pathologic response and incidence of radiation-associated toxicity. Univariable logistic regressions and stepwise multivariable logistic regressions were performed. Results Seventy-nine patients (mean age: 68.6 ± 11.2 years, 39.2% female) met inclusion criteria. Prior to neoadjuvant therapy, median T-stage was 3 (range: 1–4), median N-stage was 1 (range: 0–2), and mean tumor distance from the anal verge was 6.3cm (± 2.9). Thirty-five patients (44.3%) were using statins. Overall, 7.6% experienced pCR and 29.1% had no treatment response on pathology. Radiation-associated toxicity was 43.0%. Statin use was not associated with pCR (OR 2.71, 95%CI 0.47–15.7, p = 0.27), however on stepwise multivariable logistic regression, statin use was associated with decreased prevalence of no response (OR 0.08, 95%CI 0.01–0.43, p = 0.003). Conclusions Statins may offer a synergistic effect when given concurrently with short-course neoadjuvant radiation for rectal cancer. Further prospective study evaluating the use of statins in conjunction with neoadjuvant therapy is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".