Use of celecoxib immediately post marketing in Canada: acute or chronic pain?
Bibliographic record
Abstract
OBJECTIVES: The diffusion of innovations theory suggests that early users of innovations influence others. This study was undertaken to apply the diffusion of innovations theory to the prescribing of celecoxib and to determine if prescriber and patient characteristics differed amongst early use of celecoxib for acute pain versus chronic musculoskeletal conditions. METHODS: Using Manitobaâs population-based prescription and health care databases, diffusion time from market availability to first prescription for celecoxib was determined for each prescriber. The diffusion of prescribing curves for celecoxib in acute pain versus chronic musculoskeletal conditions were compared. Separately for acute and chronic conditions, the likelihood of being an early or late prescriber or user of celecoxib was determined according to physician factors (specialty and place of training) and patient demographics. This multivariate analysis was completed using polytomous logistic regression, with majority prescribers as the reference. RESULTS: The use of celecoxib for chronic musculoskeletal conditions demonstrated faster diffusion than for acute pain. The majority of early use of celecoxib was for chronic conditions; however 36% of first prescriptions were for acute pain, including the treatment of back pain and injuries. Early prescribers of celecoxib for acute pain were more likely than majority prescribers to be general practitioners (OR = 2.24, 95%CI: 1.53-3.29) and have hospital affiliations (OR=1.54, 95%CI: 1.04-2.27). Early users of celecoxib for chronic conditions were less likely to be low income (OR=0.56, 95%CI: 0.35-0.91). CONCLUSIONS: Immediately after market release in Canada, celecoxib was commonly prescribed for the treatment of acute pain; these prescriptions were associated with general practitioners and hospital affiliation status.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".