The effect of adjuvant chemotherapy on survival in node negative colorectal cancer with or without perineural invasion: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose It was aimed at assessing the benefits of adjuvant chemotherapy (ACT) for patients with node-negative colorectal cancer (CRC) either with or without perineural invasion (PNI). Methods We systematically searched PubMed, Cochrane Library, Embase, and Web of Science from database inception through October 1, 2023. Survival outcomes were analyzed using hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). Heterogeneity for the descriptive meta-analyses was quantified using the I2 statistic. Results Ten studies included in this review. ACT improved overall survival (OS) (HR 0.52, 95% CI 0.40–0.69) and disease-free survival (DFS) (HR 0.53, 95% CI 0.35–0.82) in PNI + patients but did not affect DFS (HR 1.13, 95% CI 0.72–1.77) in PNI- patients. A disease-specific survival (DSS) benefit with chemotherapy was observed in PNI + (HR 0.76, 95% CI 0.58–0.99) and PNI- patients (HR 0.76, 95% CI 0.57–1.00). And PNI decreased DFS (HR 1.94, 95% CI 1.52–2.47) and OS (HR 1.75, 95% CI 0.96–3.17) in node-negative CRC. Conclusions In conclusion, chemotherapy appears most beneficial for survival outcomes in node-negative patients with PNI, but may also confer some advantage in those without PNI. Systematic Review Registration Identifier INPLASY2021120103.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".