Opioid drug use in emergency and adverse outcomes among patients with chronic obstructive pulmonary disease: a multicenter observational study
Bibliographic record
Abstract
There is still debate as to the safety of non-palliative opioid administration to chronic obstructive pulmonary disease (COPD) patients punctually treated for an acute complaint. All patients over 40 presenting at two university hospital emergency departments (Montréal Qc, Grenoble Fr) from March 2008 to September 2014 with dyspnea, abdominal pain or trauma were retrieved, and COPD patients were selected. Our primary endpoint was a composite criterion including invasive ventilation and death. Comparisons between visits in which opioid drugs were prescribed and those without opioids were performed using an inverse probability of treatment and censoring weight (IPTCW) estimator to adjust for baseline confounders. A survival weighted Cox model was used. 7799 visits by COPD patients were identified, corresponding to 4173 unique patients. Opioid drug prescription was reported in 1317 (16.9%) visits. After applying IPCTW weighting, opioid prescription was found to be associated with the composite criterion of poor clinical outcomes (HR = 4.73 (2.94; 7.61), p < 0.01). When taken separately, this association remained significant for invasive ventilation and death, but not for NIV. All sensitivity analyses confirmed the association, except for patients with trauma or abdominal pain as the main complaint. This excess risk is observed whatever the route of administration.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".