Labeled NSAID hypersensitivity and the risk of opioid prescribing; an observational study
Bibliographic record
Abstract
Background NSAIDs are widely used for pain management but are second only to antibiotics in causing drug hypersensitivity reactions. Misclassification of these reactions often leads to unnecessary avoidance of the entire drug class, potentially resulting in increased opioid prescribing. This study aimed to assess the prevalence and characteristics of NSAID hypersensitivity, cross-reactivity patterns, and the association between NSAID hypersensitivity and opioid prescribing. The use of COX-2 selective inhibitors as a safe alternative was also explored. Methods A retrospective cohort study was conducted at a tertiary care hospital, including patients with documented NSAID hypersensitivity between 2016 and 2023. Data on demographics, hypersensitivity reactions, NSAID cross-reactivity, and opioid prescriptions were collected. Patients with penicillin hypersensitivity were included for comparison. Logistic regression was used to analyze the association between NSAID hypersensitivity and opioid prescribing. Results Among 319 patients with NSAID hypersensitivity, 30% ( n = 96) were classified as true allergy, 12.5% ( n = 40) as pseudo-allergy, and 57% ( n = 183) were unclassified. Cross-reactivity between NSAIDs was observed in 13%, although 52% tolerated at least one other NSAID. Patients with NSAID hypersensitivity were 62% more likely to be prescribed opioids compared to those with penicillin hypersensitivity [adjusted OR 1.62 (95% CI: 1.40–1.88), p < 0.001]. Celecoxib was underutilized, prescribed to only 10% of hypersensitive patients. Conclusion NSAID hypersensitivity is associated with increased opioid prescribing due to class-wide avoidance. Despite concerns about cross-reactivity, many patients can tolerate alternative NSAIDs. Improved classification tools and clinical decision support systems are needed to guide prescribers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".