Outcome in patients converting to psychosis following a treated clinical high risk state
Bibliographic record
Abstract
AIM: We explored 2-year outcomes in a sample of clinical high risk (CHR) patients who converted to psychosis despite receiving interventions. METHODS: Of 167 CHR patients, 18 had converted to psychosis and received treatment for their first episode of psychosis in an early intervention service over 2 years. RESULTS: Compared to patients admitted directly to the same early intervention service without having been identified as CHR prior to onset of psychosis, CHR converters were in remission for fewer months (M = 5 vs M = 10); were more likely to be prescribed more than 1 antipsychotic medication (90% vs 68%) and to receive clozapine treatment (38% vs 2%) over 2 years. CONCLUSIONS: CHR patients who convert to psychosis may be inherently more resistant to comprehensive treatment and may have poorer outcomes. Conversion to psychosis from a state of CHR can be reduced to a rate of 10%-12% following interventions, yet outcomes for patients who convert despite such interventions remain unexplored.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".