Protocol for the development of a transdiagnostic core outcome set for mental health disorders in adults: the Patient Important Outcomes in Psychiatry (PIO-Psych) Initiative
Bibliographic record
Abstract
INTRODUCTION: Mental health problems are important causes of disability and economic costs worldwide. Randomised clinical trials examining the treatment of mental health disorders measure heterogeneous outcomes, causing difficulties in data synthesis, interpretation and translation into clinical practice. The aim of the Patient Important Outcomes in Psychiatry (PIO-Psych) Initiative is to develop an overarching, transdiagnostic research-based and consensus-based core outcome set for adult mental health disorders. METHODS AND ANALYSIS: The development of the PIO-Psych transdiagnostic core outcome set will include three phases: (1) a systematic scoping review of the literature to develop the initial list of outcomes for the Delphi study; (2) a Delphi study in three rounds including people with lived experience of mental health disorders and their relatives, clinicians, researchers and others (administrators, mental healthcare policymakers, philosophers); (3) a hybrid consensus meeting to agree on the final overarching, transdiagnostic core outcome set and corresponding time points of assessment of each outcome. ETHICS AND DISSEMINATION: Ethical approval is not applicable to this study according to the Research Ethics Committee of the Capital Region of Denmark, as it is not an interventional study. All data will be reported anonymously, and it will not be possible to identify study participants. Results will be disseminated via stakeholder and research networks and peer-reviewed publications. TRIAL REGISTRATION DETAILS: The PIO-Psych Initiative was pre-registered with COMET (Core Outcome Measures for Effectiveness Trials) on 17 May 2024 (https://www.comet-initiative.org/Studies/Details/3125).
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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 | 0.206 | 0.231 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.125 | 0.031 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".