PLAN-e-PSY, a mobile application to improve case management and patient’s functioning in first episode psychosis: protocol for an open-label, multicentre, superiority, randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: The prognosis of first episode psychosis (FEP), which is a severe disorder, can be notably impaired by patients' disengagement from healthcare providers. Coordinated specialty care with case management is now considered as the gold standard in this population, but there are still challenges for engagement with subsequent functional impairments. Youth-friendly and patient-centred clinical approaches are sought to improve engagement in patients with FEP. Mobile applications are widely used by young people, including patients with FEP, and can increase the youth friendliness of clinical tools. We hypothesise that a co-designed mobile application used during case management can improve functioning in patients with FEP as compared with usual case management practices. METHODS AND ANALYSIS: A mobile case management application for planning and monitoring individualised care objectives will be co-designed with patients, caregivers and health professionals in a recovery-oriented approach. The application will be compared with usual case management practices in a multicentre, two-arm and parallel groups clinical trial. Patients will be recruited by specialised FEP teams. Impact on functioning will be assessed using the Personal and Social Performance Scale; the variation between baseline and 12 months in each group (control and active) will be the primary outcome. ETHICS AND DISSEMINATION: This study has been approved by the Inserm Institutional Review Board IRB00003888 (Comité d'évaluation éthique de l'INSERM, reference number 20-647). The results of the study will be published in a peer-reviewed journal and presented at national and international conferences. We will also communicate the results to patients and family representatives' associations. An optimised version of the application will be then disseminated through the French FEP network (Transition Network). TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: NCT04657380.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.015 |
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".