Prescribing Opioids for Chronic Noncancer Pain in Primary Care: Risk Assessment
Bibliographic record
Abstract
The use of opioids for patients with chronic noncancer pain has increased dramatically, and with increasing use there is increasing concern about the potential for abuse and addiction during long-term treatment. Clinicians should avoid viewing formal or subjective risk assessment as a means of classifying patients into 2 distinct categories: compliant patients and substance abusers. The provider who perceives a patient as compliant may have a complacent attitude toward aberrant drug-related behavior, presuming that these signs reflect inadequately controlled pain, to be addressed by dose escalation. The provider who perceives a patient as a substance abuser may refuse to provide treatment for pain, leaving the patient to seek either illicit drugs or prescribed treatment from another provider. In fact, in seemingly compliant patients, any noncompliant use of opioids presents a safety risk regardless of the explanations offered. Even in known or suspected drug abusers, chronic pain warrants the use of adequate pharmacotherapy, although treatment in such cases may exclude drugs with high abuse potential. Thus, all aberrant drug-related behavior should be addressed within a treatment plan that combines adequate pain care with suitable interventions for the aberrant behavior, following current best practice strategies. This approach is consistent with the approach taken with other health conditions, such as diabetes or hypertension, for which it is understood that noncompliance with therapy presents a risk of harm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".