A drug utilization study of thiocolchicoside‐containing medicinal products for systemic use in France and Italy: A cross‐sectional electronic medical records database study
Bibliographic record
Abstract
PURPOSE: The risk minimization measures (RMM) for systemic use of thiocolchicoside (TCC) was implemented across Europe during 2014-2016. RMM included restriction of use in age <16 years, maximum dose and duration, chronic conditions, contraindication in pregnancy, lactation or in women of childbearing potential [WOCBP] without appropriate contraception. The current Drug Utilization Study was aimed to describe the prescribing practices of TCC in France and Italy. METHOD: The study analyzed data (demographic, prescription, diagnosis, and concomitant treatment) from electronic medical record databases. It compares drug utilization during pre-implementation (baseline: year 2013) and post-implementation (years 1, 2, and 3) of RMM. This study included panels of general practitioners (FGP) and rheumatologists (FRH) in France and Italy (IGP). RESULTS: TCC was largely prescribed as adjuvant therapy in both pre-implementation (FGP: 93.5%, FRH: 88.8%, IGP: 86.6%) and post-implementation (FGP: 92.3%, FRH: 89.5%, IGP: 89.0%) periods. Prescribing patterns were different in France and Italy, with FGP and FRH mainly prescribing oral formulation (>95% and >80%, respectively), while IGP prescribing intramuscular formulation (>70%). Prescriptions to patients aged ≥16 years were >99% in all panels during both periods. An improvement was observed in compliance with treatment duration for oral formulation in the FGP panel post-implementation versus pre-implementation (66.2% vs. 46.7%; p < 0.001). There was no change in prescription rate post RMM implementation in pregnant (FGP: 0.5%, IGP: 4.7%) and in WOCBP without appropriate contraception (FGP: 89.3%, IGP: 93.4%). CONCLUSION: These results highlighted changes in prescribing practices of TCC after RMM implementation, which varied across panels and measures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".