The quality of research synthesis in surgery: the case of laparoscopic surgery for colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Several systematic reviews and meta-analyses populate the literature on the effectiveness of laparoscopic surgery for colorectal cancer. The utility of this body of work is unclear. The objective of this study was to synthesize all such systematic reviews in terms of clinical effectiveness, to appraise their quality, and to determine whether areas of duplication exist across reviews. METHODS: Systematic reviews comparing laparoscopic and open surgery for colorectal cancer were identified using a comprehensive search protocol (1991 to 2008). The primary outcome was overall survival. The methodological quality of reviews was appraised using the Assessment of Multiple Systematic Reviews (AMSTAR) instrument. Abstraction and quality appraisal was carried out by two independent reviewers. Reviews were synthesized, and outcomes were compared qualitatively. A citation analysis was carried out using simple matrices to assess the comprehensiveness of each review. RESULTS: In total, 27 reviews were included; 13 reviews included only randomized controlled trials. Rectal cancer was addressed exclusively by four reviews. There was significant overlap between review purposes, populations and, outcomes. The mean AMSTAR score (out of 11) was 5.8 (95% CI: 4.6 to 7.0). Overall survival was evaluated by ten reviews, none of which found a significant difference. Three reviews provided a selective meta-analysis of time-to-event data. Previously published systematic reviews were poorly and highly selectively referenced (mean citation ratio 0.16, 95% CI: 0.093 to 0.22). Previously published trials were not comprehensively identified and cited (mean citation ratio 0.56, 95% CI: 0.46 to 0.65). CONCLUSIONS: Numerous overlapping systematic reviews of laparoscopic and open surgery for colorectal cancer exist in the literature. Despite variable methods and quality, survival outcomes are congruent across reviews. A duplication of research efforts appears to exist in the literature. Further systematic reviews or meta-analyses are unlikely to be justified without specifying a significantly different research objective. This works lends support to the registration and updating of systematic reviews.
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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 | 0.690 | 0.887 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.045 | 0.043 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".