To Use or Not to Use Opioid Analgesia for Acute Abdominal Pain Before Definitive Surgical Diagnosis? A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Despite the existing evidence, many physicians are reluctant to use opioid analgesia for acute abdominal pain. METHODS: We performed updated conventional and network meta-analyses. For the first time to our knowledge, direct and indirect evidence of any type of opioid analgesia was estimated and compared using network meta-analysis. RESULTS: There was no significant difference in the intensity of pain between the two cohorts (mean difference (MD) = 0.43 (-0.05 to 0.91), P = 0.08). In addition, no significant difference was detected in the rate of incorrect diagnoses between the opioid analgesia and the placebo cohorts (odds ratio (OR) = 0.79 (0.54 to 1.17), P = 0.24). Network meta-analysis demonstrated that the results of direct evidence of head-to-head comparisons of opioid analgesics with placebo were in accordance with the results of conventional meta-analysis. Moreover, estimation and comparison of the indirect evidence on the four opioid analgesics did not demonstrate significant differences in effect size. CONCLUSIONS: Any type of opioid analgesic can be used safely for acute abdominal pain without risk of impairment of diagnostic accuracy.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".