A Pilot Randomized Controlled Evaluation of “Extended Specialized Early Intervention Service” vs. “Regular Care” for Longterm Management of Early Psychosis
Bibliographic record
Abstract
Introduction: Short term benefits of Specialized Early Intervention (SEI) services for treatment of first episode of psychosis (FEP) are not sustained after transfer to regular care. Optimum length of SEI services remains to be determined. Objective: To carry out a randomized controlled trial (RCT) of extending SEI service for an additional three years compared to “regular” care after both groups have received two years of SEI treatment. Hypothesis: The experimental group (extended SEI) will have better clinical (remission length and proportion) and functional outcomes and be cost effective compared to the control group (regular care). Methods: Remission (length and proportion in remission) are the primary outcomes. We aim to randomize a total of 212 patients following two years of SEI service for their FEP. Outcome evaluations to assess symptoms, functioning and service utilization are carried out at entry and every three months. In this presentation we will report only the method and preliminary results (success of urn randomization, drop out and relapse rates) on the sample recruited thus far. Results: Of the 58 patients approached 50 (86%) agreed to be randomized. Patients were young (mean age 25), mostly male (with a diagnosis of Schizophrenia Spectrum Psychosis (71%). The average length of follow up to date is 13.2 months (s.d. = 5.4). Treatment discontinuation in the experimental and control conditions were 0 and 4 (15%), respectively. Conclusion: The pilot results show the feasibility for carrying out such a study. The methodological challenges of conducting this long term RCT will be discussed.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".