Addressing clozapine under-prescribing and barriers to initiation
Bibliographic record
Abstract
Clozapine use has declined, despite its superior antipsychotic efficacy in treatment-resistant schizophrenia. Implications for clozapine underutilization include suboptimal treatment outcomes and increased hospitalizations. Many barriers preventing the use of clozapine have been described in the literature, including suboptimal knowledge and poor perceptions. The aim of this study was to assess psychiatry prescribers' perception and knowledge of clozapine. A survey was distributed to advanced practice providers, psychiatrists, and trainees (i.e. residents and fellows) at 10 medical centers within the US and Canada. The survey asked respondents about their perception of clozapine use and assessed their pharmacotherapeutic knowledge of clozapine. Two hundred eleven individual submitted completed surveys of a possible 1152; a response rate of 18.3%. There were no statistically significant differences between the advanced practice provider plus psychiatrist groups and the trainee group for most perception (eight of nine) and knowledge (eight of nine) questions. The knowledge questions with the lowest scores pertained to clozapine reinitiation and myocarditis. The majority of all respondents (144, 68.2%) felt that clozapine prescribing was a burden. Findings of this study support the need for continued clozapine education regardless of a prescriber's age/experience. Future studies to assess barriers to clozapine prescribing should extend beyond academic centers.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".