Controlling pain and reducing misuse of opioids: ethical considerations.
Bibliographic record
Abstract
OBJECTIVE: To help family physicians achieve an ethical balance in their opioid prescribing practices. QUALITY OF EVIDENCE: MEDLINE was searched for English-language articles published between 1985 and 2011. Most available evidence was level III. MAIN MESSAGE: It is essential to follow practice guidelines when prescribing opioids, except when another course of action is demonstrably justified. In addition, when considering the appropriateness of an opioid prescription, with its many ethical implications, the decision can be usefully guided by the application of the ethical principles of beneficence, nonmaleficence, respect for autonomy, and justice. As well, it is essential to keep current about legal and regulatory changes and provincial electronic registries of opioid prescriptions. CONCLUSION: Physicians need to ensure that their patients' pain is properly assessed and managed. Reaching optimal pain control might necessitate prescribing opioids. But the obligation to provide pain relief needs to be balanced with an equally important responsibility not to expose patients to risk of addiction and not to create opportunities for drug diversion, trafficking, and the addiction of others. Basic ethical principles can provide a framework to help physicians make ethically appropriate decisions about opioid prescribing.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".