Effect of regulatory interventions on agomelatine use in Spain: A multiple intervention time‐series analysis in a nationwide electronic healthcare record database
Bibliographic record
Abstract
BACKGROUND: Liver injury is an important identified risk for agomelatine and several measures were put in place to prevent and minimize such risk. The study aims to assess the impact of four interventions on the incidence of agomelatine use, particularly among patients aged ≥75 in Spain between 2011 and 2018. METHODS: Quasi-experimental interrupted time-series analysis to examine data from a nationwide electronic healthcare record database (BIFAP). Quarterly cumulative incidence of agomelatine use per 100 000 patients was calculated and the impact of four regulatory interventions was quantified. RESULTS: The incidence of agomelatine use decreased by 85% and 87% from first quarter 2011 to last quarter 2018 in patients below and above 75 years old, respectively. Regulatory actions taken were not associated with an immediate and significant falling level of use or slope. The incidence was less than expected 6 months after the first and third intervention for patients below and above 75 years old, and more than expected after the second and fourth intervention for both populations, though these analyses were underpowered to observe significant results. The downward trend became less pronounced, reaching a residual level of use, which remained stable in the last segment of the study period. CONCLUSION: New users of agomelatine decreased throughout the study period, starting before interventions took place. The effect of specific interventions might be masked by the progressive decrease tendency, constant over the study period. The effects of external factors that might overlap, unintended consequences, and issues concerning statistical modeling in situations where rates are already falling, should be considered when interpreting the results.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".