Transdiagnostic clinical staging in youth mental health: a first international consensus statement
Bibliographic record
Abstract
Recognizing that current frameworks for classification and treatment in psychiatry are inadequate, particularly for use in young people and early intervention services, transdiagnostic clinical staging models have gained prominence. These models aim to identify where individuals lie along a continuum of illness, to improve treatment selection and to better understand patterns of illness continuity, discontinuity and aetiopathogenesis. All of these factors are particularly relevant to help-seeking and mental health needs experienced during the peak age range of onset, namely the adolescent and young adult developmental periods (i.e., ages 12-25 years). To date, progressive stages in transdiagnostic models have typically been defined by traditional symptom sets that distinguish "sub-threshold" from "threshold-level" disorders, even though both require clinical assessment and potential interventions. Here, we argue that staging models must go beyond illness progression to capture additional dimensions of illness extension as evidenced by emergence of mental or physical comorbidity/complexity or a marked change in a linked biological construct. To develop further consensus in this nascent field, we articulate principles and assumptions underpinning transdiagnostic clinical staging in youth mental health, how these models can be operationalized, and the implications of these arguments for research and development of new service systems. We then propose an agenda for the coming decade, including knowledge gaps, the need for multi-stakeholder input, and a collaborative international process for advancing both science and implementation.
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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 | 0.192 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".