Preventing and managing aberrant drug-related behavior in primary care: Systematic review of outcomes evidence
Bibliographic record
Abstract
Several strategies for preventing, identifying, and responding to aberrant opioid-related behaviors are recommended in pain management guidelines. This systematic review evaluated data supporting basic strategies for addressing aberrant opioid-related behaviors. Risk reduction strategies were identified via a review of available guidelines. Systematic literature searches of PubMed (May 1, 2007-January 18, 2013) identified articles with evidence relevant to nine basic strategies. Reference lists from relevant articles were reviewed for additional references of interest. Levels of evidence for articles identified were graded on a four-point scale (strongest evidence = level 1; weakest evidence = level 4) using Oxford Centre for Evidence-Based Medicine Levels of Evidence criteria. Weak to moderate evidence supports the value of thorough patient assessment, risk-screening tools, controlled-substance agreements, careful dose titration, opioid dose ceilings, compliance monitoring, and adherence to practice guidelines. Moderate to strong evidence suggests that prescribing tamper-resistant opioids may help prevent misuse but may also have the unintended consequence of prompting a migration of users to other marketed opioids, heroin, or other substances. Similarly, preliminary evidence suggests that although recent regulatory and legal efforts may reduce misuse, they also impose barriers to the legitimate treatment of pain. Despite an absence of consistent, strong supporting evidence, clinicians are advised to use each of the available risk-mitigation strategies in combination in an attempt to minimize the risk of abuse in opioid treatment patients. Physicians must critically evaluate their opioid prescribing and not only increase their efforts to prevent substance abuse but also not compromise pain management in patients who benefit from it.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".