Misuse of and dependence on opioids: study of chronic pain patients.
Bibliographic record
Abstract
OBJECTIVE: To review the evidence on identifying and managing misuse of and dependence on opioids among primary care patients with chronic pain. QUALITY OF EVIDENCE: MEDLINE was searched using such terms as "opioid misuse" and "addiction." The few studies on the prevalence of opioid dependence in primary care populations were based on retrospective chart reviews (level II evidence). Most recommendations regarding identification and management of opioid misuse in primary care are based on expert opinion (level III evidence). MAIN MESSAGE: Physicians should ask all patients receiving opioid therapy about current, past, and family history of addiction. Physicians should take "universal precautions" that include careful prescribing and ongoing vigilance for signs of misuse. Patients suspected of opioid misuse can be treated with a time-limited trial of structured opioid therapy if they are not acquiring opioids from other sources. The trial should consist of daily to weekly dispensing, regular urine testing, and tapering of doses of opioids. If the trial fails or is not indicated, patients should be referred for methadone or buprenorphine treatment. CONCLUSION: Misuse of and dependence on opioids can be identified and managed successfully in primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.006 | 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".