International Dosage Differences in Fluoxetine Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: International differences are thought to exist in dosages used by clinicians treating mood disorders. This study examined international dosage differences in antidepressant clinical trials, using a database formed and maintained as a component of a Cochrane review of comparative clinical trials of fluoxetine. METHODS: This systematic review included 132 studies. A detailed set of methodological features and results were abstracted from the original publications and entered into an electronic database. Mean and maximum fluoxetine dosages were compared across countries. To evaluate the dosages of comparison medications, a defined daily dosage (DDD) ratio was calculated as the trial mean dosage divided by the DDD for that drug. RESULTS: Both the maximum and mean dosages for fluoxetine and comparison medications were higher in trials conducted in the US (fluoxetine weighted mean dosage 49.18 mg; 95% CI, 41.30 to 57.05), compared with trials conducted in Europe (fluoxetine weighted mean dosage 29.98 mg; 95% CI, 25.28 to 34.68). Since most clinical trials were conducted in Europe or the US, we could not determine whether different dosages tended to be used in other regions. CONCLUSIONS: International differences in prescriber behaviour may influence, and in turn be influenced by, the conduct of clinical trials. It is difficult to reconcile such differences with the principles of evidence-based medicine.
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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 | 0.226 | 0.530 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".