Emergency Physicians Ability to Recognize and Diagnose Opiate Use Disorder: A Qualitative Study
Bibliographic record
Abstract
Introduction The opioid crisis is a significant public health problem for this generation. Proper treatment of patients with opiate use disorder (OUD) during vulnerable times is vital to their engagement in opiate agonist therapy (OAT). There is limited information as to the efficacy of ED practitioners in recognition of opioid withdrawal or OUD; this research was designed to fill this gap to advance our care of vulnerable populations. Methods Interviews were conducted with seven convenience-sampled ED physicians and nurse practitioners from the Saint John Regional Hospital by providing a clinical vignette. These one-on-one, scripted interviews, conducted by the principal and co-investigator, tell us about the ED physician's understanding of OUD and withdrawal by posing questions around the presentation within the clinical vignette, as well as around general knowledge of OUD and acute withdrawal. Results All seven participants identified the patient in the case as being in opioid withdrawal but did not identify all symptoms in the vignette. Two correctly diagnosed our patient as having OUD based on the scene provided. Five physicians identified criteria that pointed toward this diagnosis but did not vocalize the connection. Only one discussed prescription of OAT as a treatment, most opting for symptom management and information on sites of self-referral for treatment. Finally, six of our interviewees expressed interest in prescribing buprenorphine but cited a need for more education around its use and initiation before feeling comfortable prescribing it. Conclusions While ED practitioners correctly recognize opiate withdrawal, there is a knowledge gap in the related diagnosis and evidence-based management of OUD. The development of education around these gaps will allow for stronger advocacy and better outcomes for this patient population.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".