Intake Assessment of Problematic Use of Medications in a Chronic Noncancer Pain Clinic
Bibliographic record
Abstract
BACKGROUND: The present article outlines the process of instituting an assessment of risk of problematic use of medications with new patients in an ambulatory chronic noncancer pain (CNCP) clinic. It is hoped that the authors' experience through this iterative process will fill the gap in the literature by setting an example of an application of the 'universal precautions' approach to chronic pain management. OBJECTIVES: To assess the feasibility and utility of the addition of a new risk assessment process and to provide a snapshot of the risk of problematic use of medications in new patients presenting to a tertiary ambulatory clinic treating CNCP. METHODS: Charts for the first three months following the institution of an intake assessment for risk of problematic medication use were reviewed. Health care providers at the Wasser Pain Management Centre (Toronto, Ontario) were interviewed to discuss the preliminary findings and provide feedback about barriers to completing the intake assessments, as well as to identify the items that were clinically relevant and useful to their practice. RESULTS: Data were analyzed and examined for completeness. While some measures were considered to be particularly helpful, other items were regarded as repetitive, problematic or time consuming. Feedback was then incorporated into revisions of the risk assessment tool. DISCUSSION: Overall, it is feasible and useful to assess risk for problematic use of medications in new patients presenting to CNCP clinics. CONCLUSION: To facilitate the practice of assessment, the risk assessment tool at intake must be concise, clinically relevant and feasible given practitioner time constraints.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".