Guidance on the clinical understanding and use of long‐acting injectable antipsychotics in Schizophrenia: Hong Kong Consensus Statements
Bibliographic record
Abstract
AIMS: There is increasing evidence showing the importance of long-acting injectable antipsychotics in the management of schizophrenia, especially in terms of improving patient medication compliance. A panel of experienced clinicians in Hong Kong mapped out a set of consensus statements with an aim to facilitate the understanding and use of long-acting injectable antipsychotics among local physicians. METHODS: Eight discussion areas regarding long-acting injectable antipsychotics were selected by the chairman of the consensus group. A series of meetings were held for the panelists to discuss the published literature and their clinical experience, followed by the drafting of consensus statements. At the final meeting, each consensus statement was voted on anonymously by all members based on its practicability of recommendation in Hong Kong. RESULTS: A total of 12 consensus statements on the rational use of long-acting injectable antipsychotics were established and accepted by the consensus group. CONCLUSION: The consensus statements aim to provide practical guidance for Hong Kong physicians on the use of long-acting injectable antipsychotics in schizophrenia patients. These statements may also serve as a reference for doctors in other parts of the Asia-Pacific region.
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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 | 0.085 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".