Do pain specialists meet the needs of the referring physician? A survey of primary care providers
Bibliographic record
Abstract
OBJECTIVE: To study the factors that influence the use of opioids in the management of chronic noncancer pain (CNCP) by primary care providers (PCPs) for patients returning from a pain specialist. DESIGN: A survey of PCPs. SETTING: Two physician groups in the Minneapolis-St. Paul metropolitan area. PARTICIPANTS: Two seventy-six PCPs surveyed and 80 surveys returned. MAIN OUTCOME MEASURES: Participants rated the importance of specific concerns regarding the role of pain specialists and the use of opioids in the management of CNCP. Past experience with pain specialists, comfort using opioids, and opinions regarding a trilateral opioid agreement were also examined. RESULTS: The top concerns for PCPs were as follows: the use of opioids in patients with chemical dependency or psychological issues, the escalation of opioid dosing, and the use of opioids in pain states without objective findings. They also ranked highly the importance of coordinating the return of patients from a pain specialist with explicit opioid instructions and the availability of consultation by phone or a timely follow-up visit. PCPs were supportive of the concept of a trilateral opioid agreement. CONCLUSIONS: PCPs have significant concerns regarding the prescribing of opioids in CNCP. They desire closer collaboration with pain specialists, including more explicit plans of care when patients are transferred back to them. The trilateral agreement may provide one framework for better collaboration.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".