Encouraging physician appropriate prescribing of non-steroidal anti-inflammatory therapies: protocol of a randomized controlled trial [ISRCTN43532635]
Bibliographic record
Abstract
BACKGROUND: Traditional non-steroidal anti-inflammatory drugs (NSAIDs) are a widely used class of therapy in the treatment of chronic pain and inflammation. The drugs are effective and can be relatively inexpensive thanks to available generic versions. Unfortunately the traditional NSAIDs are associated with gastrointestinal complications in a small proportion of patients, requiring costly co-therapy with gastro-protective agents. Recently, a new class of non-steroidal anti-inflammatory agents known as coxibs has become available, fashioned to be safer than the traditional NSAIDs but priced considerably higher than the traditional generics. To help physicians choose appropriately and cost-effectively from the expanded number of anti-inflammatory therapies, scientific bodies have issued clinical practice guidelines and third party payers have published restricted reimbursement policies. The objective of this study is to determine whether an educational intervention can prompt physicians to adjust their prescribing in accordance with these expert recommendations. METHODS: This is an ongoing, randomized controlled trial. All primary care physicians in Manitoba, Canada have been randomly assigned to a control group or an intervention study group. The educational intervention being evaluated consists of an audit and feedback mechanism combined with optional participation in a Continuing Medical Education interactive workshop. The primary outcome of the study is the change, from pre-to post-intervention, in physicians' appropriate prescribing of non-steroidal anti-inflammatory therapies for patients requiring chronic treatment. Three classes of non-steroidal anti-inflammatory therapies have been identified: coxib therapy, traditional NSAID monotherapy, and traditional NSAID therapy combined with gastro-protective agents. Appropriate prescribing is defined based on international clinical practice guidelines and the provincial drug reimbursement policy in Manitoba.
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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 | 0.045 | 0.039 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".