Barriers to using clozapine in treatment-resistant schizophrenia: systematic review
Bibliographic record
Abstract
Aims and methodTo systematically review the literature on barriers to the use of clozapine and identify any interventions for optimizing clozapine use in treatment-resistant schizophrenia. Journal databases were searched from 1972 to March 2018. The following search terms were used: treatment-resistant schizophrenia, clozapine, barriers, use, prescription rates, implementation, clozaril and prescribing practices. Following a review of the literature, 15 papers were included in the review. RESULTS: The major barriers that were identified included mandatory blood testing, fear of serious side-effects and lack of adherence by the patients, difficulty in identifying suitable patients, service fragmentation, and inadequate training in or exposure to using clozapine.Clinical implicationsIn view of consistent evidence across the studies on inadequate knowledge and skills as a significant barrier, we suggest that a certification requiring competence in initiating and managing side-effects of clozapine becomes a mandatory requirement in training programmes.Declarations of interestNone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".