Development of a registry for monitoring psychotropic drug prescriptions: aims, methods and implications for ordinary practice and research
Bibliographic record
Abstract
In psychiatry, individual-based registries have provided key information on risks and benefits associated with the use of psychotropic drugs but they have rarely been employed for monitoring and evaluating the everyday prescribing of psychopharmacological treatments. This article describes the cultural background that gave impetus to the idea of registering all prescriptions of psychotropic drugs dispensed by physicians working in the South Verona community mental health service, and presents the methodology employed to develop such a registry in a community psychiatric service where a psychiatric case register (PCR) has been operating since 1978. We developed a registry including every patient receiving psychotropic medications in ordinary practice. This registry is linked to the PCR in order to obtain data on social and demographic characteristics, clinical symptoms, diagnosis, use of services, and outcomes. No exclusion criteria are allowed--anyone receiving treatment is automatically included. This system, which can link drug and service-use data with hard outcome indicators, can generate information on the proportion of subjects discontinuing treatment, switching medication because of side-effects, recovery or inefficacy, as well as on the proportion of subjects failing to return to the physician, and the proportion of patients who improve. The innovative aspect of this approach is that this registry is developed, organized and used by physicians interested in monitoring their clinical practice and in providing patients, relatives and the public with accurate information on drug use in their specific context of care.
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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 | 0.158 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".