Predictors of rate and time to remission in first-episode psychosis: a two-year outcome study
Bibliographic record
Abstract
BACKGROUND: The evidence regarding the independent influence of duration of untreated psychosis (DUP) on rate and time to remission is far from unequivocal. The goal of the current study was to examine the role of predictors for rate and time to remission in first-episode psychosis (FEP). METHOD: The differential effect of age, gender, age of onset, duration of untreated psychosis (DUP), duration of untreated illness (DUI), pre-morbid adjustment, co-morbid diagnosis of substance abuse and adherence to medication on the rate of and time to remission were estimated using a logistic and Poisson regression, and survival analysis respectively, in FEP patients. RESULTS: In a sample of 107 FEP patients 82.2% achieved remission over a period of 2 years after a mean of 10.3 weeks (range 1-72). Regression analysis, based on complete data on all variables of interest (n=80), showed status of remission to be positively influenced by better pre-morbid adjustment (RR 0.57, 95% CI 0.34-0.95, p<0.05), later age of onset (RR 1.09, 95% CI 1.05-1.13, p<0.0001), higher level of adherence to medication (RR 1.96, 95% CI 1.38-2.76, p<0.001) and shorter DUI (RR 0.99, 95% CI 0.997-0.999, p<0.005). Time to remission was influenced by age of onset (HR 1.04, 95% CI 1.00-1.08, p<0.04) and adherence to medication (HR 1.58, 95% CI 1.11-2.23, p<0.01). CONCLUSIONS: Improving adherence to medication early in the course of treatment may be an important intervention to improve short-term outcome.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".