Adverse Outcomes and Opioid Analgesic Administration in Acute Abdominal Pain
Bibliographic record
Abstract
UNLABELLED: To the authors' knowledge, no outcome-based, randomized clinical trial of the safety of opioid analgesics in acute abdominal pain exists. OBJECTIVES: 1) To assess the feasibility of a randomized clinical trial of opioid safety by estimating the adverse outcome rate among patients with abdominal pain severe enough to necessitate opioid analgesics. 2) To explore the association of opioid administration with adverse outcomes in acute abdominal pain. METHODS: The authors conducted a prospective observational study of emergency department (ED) abdominal pain patients, and followed them by telephone at three weeks to determine whether an adverse outcome occurred (defined as obstruction, perforation, ischemia, hemorrhage, peritonitis, sepsis, or death). A logistic regression of factors predicting adverse outcome was performed. RESULTS: Adverse outcomes occurred in 67 of 860 abdominal pain patients (7.8%, 95% CI = 6.1% to 9.8%), and 252 of 860 (29%) received opioids. The adverse outcome rate was 12.7% (95% CI = 9.0% to 17.0%) among patients who received opioids. Variables predictive of adverse outcome in logistic regression included: ED diagnosis of adverse outcome (OR 12.4), age (OR 1.6 per decade), fever (OR 4.6), received opioids (OR 2.1), pain duration (OR 1.5 per day), and leukocytosis (OR 2.0). CONCLUSIONS: A clinical trial would need to randomize more than 1,500 patients to establish the equivalent adverse outcome rates of opioids and placebo: the sample size of all existing studies combined is insufficient to make such a conclusion. Although opioids were associated with a higher adverse outcome rate in this logistic regression, the authors believe this may be due to confounding by pain severity. They emphasize that the study's design precludes conclusion of a causal link. No change in clinical practice is warranted. A randomized clinical trial of sufficient size to definitively resolve this issue is needed.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".