Clinical Staging: A Heuristic and Practical Strategy for New Research and Better Health and Social Outcomes for Psychotic and Related Mood Disorders
Bibliographic record
Abstract
Most mental illnesses emerge during adolescence and early adulthood, with considerable associated distress and functional decline appearing during this critical developmental phase. Our current diagnostic system lacks therapeutic validity, particularly for the early stages of mental disorders when symptoms are still emerging and intensifying and have not yet stabilized sufficiently to fit the existing syndromal criteria. While this is, in part, due to the difficulty of distinguishing transient developmental or normative changes from the early symptoms of persistent and disabling mental illness, these factors have contributed to a growing movement for the reform of our current diagnostic system to more adequately inform the choice of therapeutic strategy, particularly in the early stages of a mental illness. The clinical staging model, which defines not only the extent of progression of a disorder at a particular point in time but also where a person lies currently along the continuum of the course of an illness, is particularly useful as it differentiates early, milder clinical phenomena from those that accompany illness progression and chronicity. This will not only enable clinicians to select treatments relevant to earlier stages of an illness, where such interventions are likely to be more effective and less harmful than treatments delivered later in the course of illness, but also allow a more efficient integration of our rapidly expanding knowledge of the biological, social, and psychological vulnerability factors involved in the development of mental illness into a useful diagnostic framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".