Antipsychotic use in a first episode psychosis program
Bibliographic record
Abstract
UNLABELLED: Objective. To conduct a naturalistic, longitudinal study of prescribing patterns of atypical neuroleptics in predominantly drug-naïve first episode non-affective psychosis patients. Methods. Patients with a first episode psychosis were prescribed an antipsychotic as part of a clinical protocol and followed-up for 2 years. Comparisons were made between risperidone and olanzapine, the two most commonly prescribed antipsychotics. Socio-demographic variables and clinical characteristics such as diagnosis, duration of untreated illness and psychosis and level of positive and negative symptoms were assessed using well-established methods. We examined the first antipsychotic given, starting dose, time taken to start and to reach the maximum dose, time on first medication, maximum dose, medication change and concomitant medication use. Results. One hundred and ninety-three consecutive patients consented to start on antipsychotic. The results are provided for risperidone (N = 133) and olanzapine (N=38). The time to initiate antipsychotic medications was significantly longer for outpatients than inpatients. There were no differences between the two groups for time taken to reach the maximum dose, drop out rates or concomitant medication use. The percentage of patients taking an antipsychotic agent at any given time was high (range 79-91%), but half of the patients had changed from their first antipsychotic by 6 months. CONCLUSIONS: The reality of clinical practice can be much different than rigid protocols or treatment algorithms of pre-marketing studies or clinical trials. In this sample of first episode psychosis patients, although the majority of patients remained on an antipsychotic, changes in medication over the first 2 years were common. Polypharmacy was not a common practice.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".