Mapping Common Psychiatric Disorders
Bibliographic record
Abstract
CONTEXT Clinical experience and factor analytic studies suggest that some psychiatric disorders may be more closely related to one another, as indicated by the frequency of their co-occurrence, which may have etiologic and treatment implications. OBJECTIVE To construct a virtual space of common psychiatric disorders, spanned by factors reflecting major psychopathologic dimensions, and locate psychiatric disorders in that space, as well as to examine whether the location of disorders at baseline predicts the prevalence and incidence of disorders at 3-year follow-up. DESIGN, SETTING, AND PATIENTS A total of 34 653 individuals participated in waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. MAIN OUTCOME MEASURES The distance between disorders at wave 1, calculated using the loadings of the factors spanning the space of disorders as coordinates. This distance was correlated with the adjusted odds ratios for age, sex, and race/ethnicity of the prevalence and incidence of Axis I disorders in wave 2, with the aim of determining whether smaller distances between disorders at wave 1 predicts higher disorder prevalence and incidence at wave 2. RESULTS A model with 3 correlated factors provided an excellent fit (Comparative Fit Index = 0.99, Tucker-Lewis Index = 0.98, root mean square error of approximation = 0.008) for the structure of common psychiatric disorders and was used to span the space of disorders. Distances ranged from 0.070 (between drug abuse and alcohol dependence) to 1.032 (between drug abuse and dysthymia). The correlation of distance between disorders in wave 1 with adjusted odds ratios of prevalence in wave 2 was -0.56. The correlation of distance in wave 1 with adjusted odds ratios of incidence in wave 2 was -0.57. CONCLUSIONS Mapping psychiatric disorders can be used to quantify the distances among disorders. Proximity in turn can be used to predict prospectively the incidence and prevalence of Axis I 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".