Electronic monitoring of forensic patients
Bibliographic record
Abstract
antipsychotic medications are effective and recommended treatment for active psychotic symptoms, 1 though there is not so much evidence for the long term (i.e.several years of antipsychotic treatments 3 ).Additionally, the clinical use of these medications is not always straightforward because of their known side-effects and the fact that, in all psychiatric disorders and other illnesses in medicine, there are always patients who do not want to take the recommended treatment.This seems to have been the case in the trial pointed out by Bindman & Kripalani.4 When considering the long-term effects of antipsychotics, it is evident that the long-term treatment of psychosis needs to be developed further.We agree that it would be dangerous to see different treatments as alternatives to each other, and it has been shown that in psychiatry a combination of different treatments is, in general, more effective than any of them alone.5 Psychotherapy in the early phase of illness could be effective not only in preventing psychosis at prodromal phase, but also in enhancing adherence to antipsychotic medication. 1 Current treatment guidelines do not suggest that treatment of first-episode psychosis should include only antipsychotic medication without psychosocial treatment, but rather state that medication is one of the cornerstones of psychosis treatment.We believe there is still a lot to do in developing both medication and psychosocial treatments for schizophrenia, and hopefully active research can support this development.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".