Impact of Health Authority Control Measures Aimed at Reducing the Illicit Use of Anabolic-Androgenic Steroids
Bibliographic record
Abstract
OBJECTIVE: To evaluate two interventions on anabolic-androgenic-steroids (AAS) dispensation in retail pharmacies. MATERIAL AND METHODS: The study was conducted in a north-western region of Spain. Data were the AAS supplied by wholesale drug distributors to retail pharmacies over a period of 102 months. It is designed as an ecological time-series study; the dependent variables were daily defined doses per 1,000 inhabitants per day of each drug. The two interventions evaluated were: (1) an inspection program intended for those retail pharmacies where there was an irregular dispensation and (2) a regulation put forth forcing these pharmacies to carry out additional registers. The medications studied were stanozolol, nandrolone, methenolone, testosterone and mesterolone. RESULTS: The pre-intervention use of AAS displayed a rising trend. There was an immediate reduction of 30.56% after the first intervention, and a further reduction of 35.25% after the second. There was a seasonal pattern of use in the pre-intervention period, pointing to an increased demand at the end of spring and beginning of summer. The most abused drugs were stanozolol and nandrolone. CONCLUSION: The health actions were very effective, in that they brought about a sharp reduction in the illicit use of AAS. These interventions could be applied to other drugs in which abuse were detected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".