Overcoming the barriers to identifying and managing treatment-resistant schizophrenia and to improving access to clozapine: A narrative review and recommendation for clinical practice
Bibliographic record
Abstract
Clozapine is the only approved antipsychotic for treatment-resistant schizophrenia (TRS). Although a large body of evidence supports its efficacy and favorable risk-benefit ratio in individuals who have failed two or more antipsychotics, clozapine remains underused. However, variations in clozapine utilization across geographic and clinical settings suggest that it could be possible to improve its use. In this narrative review and expert opinion, we summarized information available in the literature on the mechanisms of action, effectiveness, and potential adverse events of clozapine. We identified barriers leading to discouragement in clozapine prescription internationally, and we proposed practical solutions to overcome each barrier. One of the main obstacles identified to the use of clozapine is the lack of appropriate training for physicians: we highlighted the need to develop specific professional programs to train clinicians, both practicing and in residency, on the relevance and efficacy of clozapine in TRS treatment, initiation, maintenance, and management of potential adverse events. This approach would facilitate physicians to identify eligible patients and offer clozapine as a treatment option in the early stage of the disease. We also noted that increasing awareness of the benefits of clozapine among healthcare professionals, people with TRS, and their caregivers can help promote the use of clozapine. Educational material, such as leaflets or videos, could be developed and distributed to achieve this goal. The information provided in this article may be useful to improve disease burden and support healthcare professionals, patients, and caregivers navigating the complex pathways to TRS management.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".