Pain Management in Primary Care: Strategies to Mitigate Opioid Misuse, Abuse, and Diversion
Bibliographic record
Abstract
Pain is among the most common reasons patients seek medical attention, and the care of patients with pain is a significant problem in the United States. Acute pain (mild-to-moderate intensity) represents one of the most frequent complaints encountered by primary care physicians (PCPs) and accounts for nearly half of patient visits. However, the overall quality of pain management remains unacceptable for millions of US patients with acute or chronic pain, and underrecognition and undertreatment of pain are of particular concern in primary care. Primary care physicians face dual challenges from the emerging epidemics of undertreated pain and prescription opioid abuse. Negative impacts of untreated pain on patient activities of daily living and public health expenditures, combined with the success of opioid analgesics in treating pain provide a strong rationale for PCPs to learn best practices for pain management. These clinicians must address the challenge of maintaining therapeutic access for patients with a legitimate medical need for opioids, while simultaneously minimizing the risk of abuse and addiction. Safe and effective pain management requires clinical skill and knowledge of the principles of opioid treatment as well as the effective assessment of risks associated with opioid abuse, addiction, and diversion. Easily implementable patient selection and screening, with selective use of safeguards, can mitigate potential risks of opioids in the busy primary practice setting. Primary care physicians can become advocates for proper pain management and ensure that all patients with pain are treated appropriately.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".