Clinical staging models: From general medicine to mental disorders
Bibliographic record
Abstract
Summary Revisions of international classification systems for mental disorders have focused on improving the reliability of diagnostic criteria. However, the uncertain validity of the current diagnostic categories means that they do not always fulfil their key purposes, namely to guide treatment and predict outcomes. This is especially true when traditional diagnostic approaches are applied to adolescents and young adults with emerging illnesses. A clinical staging model, similar to those used in general medicine, could improve diagnosis in psychiatry and aid treatment decision-making, especially if applied to individuals aged about 15–25 years, which is the peak age range for the onset of severe mental disorders. Staging models may offer a new framework for the development of interventions with high benefit and low risk, and for research into neurobiological and psychosocial risk factors. However, this approach is not without controversy: some experts oppose its introduction, some argue that it represents a transdiagnostic model, and some suggest it is only viable if disorder-specific models are used. Learning Objectives • Gain awareness of some limitations of current approaches to psychiatric diagnosis • Review the basic principles of clinical staging models used in general medicine • Understand current research on the use of staging models in psychiatry, and attempts to apply these models to bipolar 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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".