Can we improve the clinical and research utility of staging frameworks for youth mental health?
Bibliographic record
Abstract
Globally, 75% of depressive, bipolar, and psychotic disorders emerge by age 25. These disorders are often preceded by non-specific symptoms or attenuated clinical syndromes and help-seeking youth typically present with co-occurring mental disorders and/or physical comorbidities. This has led many youth mental health services to adopt trans-diagnostic clinical staging models as these offer a framework for classifying and understanding the multi-dimensional and dynamic nature of emerging mental disorders and inform the selection of treatment interventions. However, given evidence of ongoing challenges in applying trans-diagnostic staging frameworks in research and clinical practice we suggest some refinements to the model to enhance reliability, consistent recording and utility. The key proposal is to introduce two additional concepts namely within stage heterogeneity and stage modifiers, with the latter categorized into factors associated with Progression (potential predictors of stage transition and illness trajectories) and Extension (characteristics that add complexity to selection of current treatments).
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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 | 0.300 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".