O8.4. THE EFFECT OF EARLY MEDICATION DISCONTINUATION ON LONG-TERM CLINICAL OUTCOME IN FIRST EPISODE PSYCHOSIS
Bibliographic record
Abstract
Clinical decision to dis/continue antipsychotics in patients remitted from first-episode psychosis is important. Existing short-term evidence suggests that patients who discontinued antipsychotics had more relapses. Data on long-term outcomes are lacking; with only one open-label study suggesting better long-term recovery outcome in patients who had early medication discontinuation. We examined the long-term effect of early medication discontinuation in year 2 following first-episode remission for patients with no residual psychotic symptoms. We followed-up 178 first-episode psychosis patients who participated in a 1-year randomized controlled trial (RCT) on medication discontinuation. Patients were randomized into receiving either a medication maintenance group or a placebo discontinuation group. After the RCT, all patients received usual psychiatric care. Poor long-term clinical outcome was defined as a composite of persistent psychotic symptoms, a requirement for clozapine, or suicide. There were no differences between patients who were included (n=142) and excluded (n=36) from the study with regard to their baseline demographics, clinical and functioning. At 10 years, more patients in the early discontinuation group (35/89, 39%) had poor clinical outcome than patients in the maintenance group (19/89, 21%) (P<0.01). Relapse during the RCT has partly mediated the significant relationship between early medication discontinuation and poor outcome at 10-year. Whether to discontinue medication following successful treatment of first episode psychosis is a difficult clinical decision. In first episode psychosis with a full initial response to antipsychotic treatment, continued need for medication is important for the first three years after starting treatment, to prevent relapse, and decrease the risk for a poor long-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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".