Ketorolac Administration After Colorectal Surgery Increases Anastomotic Leak Rate: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
Objective This meta-analysis aimed to evaluate whether ketorolac administration is associated with an increased anastomotic leak rate after colorectal surgery. Methods The literature was searched using the Web of Science, Embase, and PubMed databases, and the search ended on May 31, 2020. The Newcastle–Ottawa Scale was used to assess methodological quality. Statistical heterogeneity was assessed using the Chi-square Q test and I2 statistics. Subgroup analysis was performed, and Egger's test was used to assess publication bias. Results This meta-analysis included seven studies with 400,822 patients. Our results demonstrated that ketorolac administration after surgery increases the risk of anastomotic leak [OR = 1.41, 95% CI: 0.81–2.49, Z = 1.21, P = 0.23]. Low heterogeneity was observed across these studies (I2 = 0%, P = 0.51). The results of subgroup analysis showed that the use of ketorolac in case–control and retrospective cohort studies significantly increased the risk of anastomotic leak (P < 0.05). Furthermore, the subgroup analysis revealed that ketorolac use increased anastomotic leak rate in patients in the United States and Canada, and ketorolac plus morphine use did not increase anastomotic leak rate in Taiwanese patients (P < 0.05). No significant publication bias was observed (P = 0.126). Moreover, the analysis of risk factors related to anastomotic leak rate indicated that the total use of ketorolac did not increase the risk of anastomotic leak similar to the control group (P > 0.05). Conclusion The meta-analysis indicates that the use of ketorolac increases the risk of anastomotic leak after colorectal surgery. Systematic Review Registration PROSPERO, identifier CRD42020195724.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".