<p>Characteristics of physicians who prescribe opioids for chronic pain: a meta-narrative systematic review</p>
Bibliographic record
Abstract
Background: The primary objective of this systematic review was to identify the characteristics of physicians who prescribe opioids to adults with chronic pain. This review was limited to studies examining fully-trained physicians, as relevant characteristics of resident physicians and non-physician clinicians may differ. Methods: A comprehensive search of databases from January 1, 1980 to December 5, 2017 was conducted. Eligible study designs included (1) randomized trials; (2) nonrandomized prospective and retrospective studies; and (3) cross-sectional observational studies. The risk of bias in the included studies was assessed using an adapted version of the Newcastle-Ottawa Scale for cross-sectional studies. A total of 2508 records were screened and 22 studies met inclusion criteria. The majority of studies were cross-sectional (n=20) and the total number of participants was 8433. Results: The risk of bias was high overall. The majority of physicians were confident managing and prescribing opioids for chronic pain but had high levels of dissatisfaction. Physicians reported high awareness of the potential for opioid misuse and were concerned about inadequate prior training in pain management. The majority of physicians were less likely to prescribe for patients with a history of substance abuse and reported major concerns about regulatory scrutiny. Conclusion: This systematic review provides the foundation for the development of prospective studies aimed at further elucidating the constellation of mechanisms that influence physicians who manage pain and prescribe opioids. Keywords: systematic review, opioid, prescription, physician characteristics
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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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".