Survival rate and Toxicity (GI and Hematology) of Neoadjuvant Therapies for Locally Advanced Rectal Cancer Patients: A Systematic Review and Meta-analysis Comparing Long-course Chemoradiation versus Short-course Radiation with Consolidation Chemotherapy
Bibliographic record
Abstract
Background: Neoadjuvant long-course chemoradiation (LCCRT) has been the conventional approach for managing locally advanced rectal cancer (LARC). However, short-course radiation followed by consolidation chemotherapy (SCRT-CC) has gained attention as an alternative strategy that may improve oncologic outcomes while optimizing treatment duration and tolerance. Objective: To compare survival outcomes and treatment-related gastrointestinal and hematologic toxicities between SCRT-CC and LCCRT in adult patients with LARC through a systematic review and meta-analysis. Methods: Following PRISMA guidelines, a systematic search of PubMed, Scopus, and Web of Science (January 2020–December 2025) identified randomized controlled trials and prospective/retrospective cohort studies evaluating SCRT-CC versus LCCRT. The primary outcome was overall survival; secondary outcomes included gastrointestinal (nausea, vomiting, diarrhea) and hematologic toxicities. Risk of bias was assessed using ROB-1 for randomized trials and the Newcastle–Ottawa Scale for observational studies. Pooled hazard ratios (HRs) and odds ratios (ORs) were calculated using random- or fixed-effects models based on heterogeneity (I²). Results: Nine studies (n = 2,869) met inclusion criteria, comprising 1,331 patients treated with SCRT-CC and 1,538 with LCCRT. SCRT-CC demonstrated a favorable trend in overall survival (HR: 0.91; 95% CI: 0.79–1.06), which became statistically significant following sensitivity analysis (HR: 0.78; 95% CI: 0.65–0.94). Hematologic toxicity did not differ significantly between groups (OR: 0.95; 95% CI: 0.34–2.65). Gastrointestinal toxicity rates were also comparable, although heterogeneity was considerable among studies. Conclusion: SCRT-CC provides survival outcomes comparable or superior to LCCRT without increasing hematologic or gastrointestinal toxicity, supporting its role as an effective neoadjuvant option for LARC. Treatment selection should still be individualized based on patient tolerance and institutional expertise. Larger, standardized clinical trials are warranted to further refine optimal sequencing strategies.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".