The European Brain Council Value of Treatment studies in depression and autism
Bibliographic record
Abstract
European Psychiatry has partnered with the European Brain Council (EBC) to present a collection of studies that showcase the breadth of the work the Council supports.The EBC (https://www.braincouncil.eu/) is a network of scientific and professional societies, patient organizations, and commercial companies that support research aimed at improving the lives of Europeans suffering from brain disorders.In 2022, the EBC celebrated 20 years of service to the field, during which it has fostered discussions and collaborations to strengthen and harmonize European mental health research and care.This collection showcases the work undertaken as part of the EBC project on the Value of Treatment (VOT2) which was initiated in 2019.As clearly outlined by Simon and colleagues, the VOT2 project combines care pathways analysis and economic analysis to identify treatment gaps, evaluate potential outcomes and costs of optimized care, and offer policy recommendations in collaboration with the EBC's scientific societies and patient organizations.The collection demonstrates the utility of the VOT2 research framework when applied to major depressive disorder (MDD) and autism spectrum disorders (ASD), two prevalent and disabling disorders.
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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 | 0.084 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".