Neoadjuvant Short-Course Radiotherapy Followed by Consolidation Chemotherapy before Surgery for Treating Locally Advanced Rectal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Neoadjuvant short course radiotherapy (SCRT) followed by consolidation chemotherapy (CCT) is an alternative treatment for locally advanced rectal cancer (LARC). We performed this systematic review and meta-analysis to explore the tumor response and oncological outcomes of this new approach compared to conventional chemoradiotherapy (CRT). An online search of the PubMed, Embase, and Cochrane Library databases was performed. This review included 7507 patients from 14 different cohorts. The pCR rate was higher with SCRT + CCT than that with CRT (RR: 1.60; 95% CI: 1.35−1.91; p < 0.01). SCRT + CCT provided a higher ypN0 response (RR: 1.06; 95% CI: 1.01−1.12; p = 0.02). There were no differences in R0 resection and positive CRM rates; however, more sphincter-preservation surgeries were performed in the SCRT + CCT arm (RR: 1.06; 95% CI: 1.01−1.11; p = 0.02). There was no difference in the OS and DFS between the SCRT + CCT and the CRT arms (OS: HR: 0.85, p = 0.07; DFS: HR: 0.88, p = 0.08). The compliance and toxicity were comparable between the SCRT and CRT groups. In the subgroup analysis, patients who underwent four or more cycles of CCT had better pCR and DFS events. Therefore, SCRT followed by consolidation chemotherapy might be an effective alternative treatment for LARC.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".