Differentiating DSM-IV Anxiety and Depressive Disorders in the General Population: Comorbidity and Treatment Consequences
Bibliographic record
Abstract
OBJECTIVE: To attempt, for the first time, to apply a positive and differential diagnosis process in the general population during interviews using DSM-IV classification to ascertain the profile and occurrence of concomitant mental disorders. METHOD: A representative sample of 1832 individuals aged 15 years or older living in the metropolitan area of Toronto were interviewed by means of telephone interviews. The participation rate was 72.8%. RESULTS: Overall, 13.2% (n = 242) of the sample had either a mood disorder (n = 127; 6.9%) or an anxiety disorder (n = 170; 9.3%) at the time of their interview. The prevalence was higher among women (16.5%) than among men (9.7%), with an odds ratio of 1.8. The comorbidity of mood and anxiety disorders was found in 3% (n = 55) of the sample. Less than one-third of respondents with a mood and/or anxiety disorder were being treated by a physician for a mental disorder. However, these individuals were greater consumers of health care services. Most of them consulted a physician an average of 5 times in the past year. Individuals on medication diagnosed with a mood and an anxiety disorder consulted a physician an average of 12 times in the past year. Only 13% of them were treated with antidepressants and under 9% with anxiolytics. CONCLUSIONS: More than 70% of subjects with a mood disorder also complained of insomnia. With the differential process, 12% of the subjects manifesting a full-fledged anxiety disorder were diagnosed with only a mood disorder because the anxiety occurred only in the course of the mood disorder. About two-thirds of the subjects diagnosed in this study were undiagnosed and untreated by their physician.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".