Bibliographic record
Abstract
Patients with first episode psychoses (FEP) have better chances of recovery compared to schizophrenia. Early stages of psychoses are critical in predicting its course and intervention early in the course of illness is important. Outcome of FEP can be improved by intervening early in the course of the illness. The implementation of early treatment for first episode and untreated psychosis is key to reducing the burden of disability due to psychotic disorders. However, the complexity of the Indian healthcare system and differences in cultural context means that simple ‘transplantation’ of western interventions is virtually impossible. Early intervention services focus specifically on reducing the duration of untreated psychosis (DUP), enhancing therapeutic engagement, and improving clinical and social outcomes by providing care in community-based, low-stigma settings. UK and Canada have led the development of early interventions in psychosis nationally and internationally. Combining the expertise from the UK, Canada and India, the Warwick-India-Canada (WIC) Project aimed to improve the health, wellbeing and functioning, and reduce the burden for those with psychotic disorders in India. The focus was tailoring evidence-informed interventions to the Indian socio-cultural context to: 1) transform the outcomes of psychotic disorders; 2) provide high quality research evidence for clinicians and policy makers; and 3) build research capacity, including new methodologies such as economic evaluation and implementation science. As part of the WIC project, two clinical sites – All India Institute of Medical Sciences (AIIMS), New Delhi and Schizophrenia Research Foundation (SCARF), Chennai used common assessment and outcome measures and standard management protocol for patients with FEP. By using validated and reliable assessment and outcomes measures, we planned cross-country and cross- setting comparisons and provide information to develop culturally appropriate, accessible, and acceptable interventions, which can be robustly tested for clinical and economic effectiveness, to improve psychosis outcomes in India and other LMIC. In this symposium, we share assessment and management protocol used for the FEP, their results and implications in two centres in India Background of the WIC study: SPS Study Protocol for First Episode Psychosis: RT Comparison of two centers: MS Implications of the study: RKC
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.598 | 0.299 |
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".