Do the Trajectories of Bipolar Disorder and Schizophrenia Follow a Universal Staging Model?
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to address the question of whether a universal staging model of severe psychiatric disorders is a viable direction for future research by examining the extant literature. METHOD: A narrative review was conducted of the relevant historical, conceptual, and empirical literature pertaining to the clinical trajectory of bipolar disorder and schizophrenia and issues relevant to staging. RESULTS: There is substantive evidence that classic recurrent bipolar disorder is separable from schizophrenia on the basis of family history, developmental and clinical course, treatment response, and neurobiological findings. However, because of the intrinsic heterogeneity of diagnostic categories that has been amplified by recent changes in psychiatric taxonomy, key distinctions between the groups have become obfuscated. While mapping risk and illness markers to emerging psychopathology is a logical approach and may be of value for some psychiatric disorders and/or their clinical subtypes, robust evidence supporting identifiable stages per se is still lacking. Presently, even rudimentary stages such as prodromes cannot be meaningfully applied across different disorders and no commonalities can be found for the basis of universal staging. CONCLUSIONS: Advances in the prediction of risk, accurate early illness detection, and tailored intervention will require mapping biomarkers and other risk indicators to reliable clinical phases of illness progression. Given the capricious nature of mood and psychotic disorders, this task is likely to yield success only if conducted in narrowly defined subgroups of individuals at high risk for specific illnesses. This approach is diametrically opposite to that being promulgated by proponents of a universal staging model.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".