Rates of Detection of Mood and Anxiety Disorders in Primary Care
Bibliographic record
Abstract
Article Abstract Objective: To determine the incidence of major depressive disorder, bipolar disorder, panic disorder, social anxiety disorder, and generalized anxiety disorder and to assess their detection rates in the Canadian primary care setting. Method: The descriptive, cross-sectional study was conducted in 7 primary care clinics in 3 Canadian provinces, Ontario, British Columbia, and Nova Scotia, from December 6, 2005, to May 5, 2006. Patients in clinic waiting rooms who consented to participate in the study were administered the Mini International Neuropsychiatric Interview (MINI) (N = 840). These patients†medical charts were then reviewed for evidence of previous diagnosis of a mood or anxiety disorder. Misdiagnosis was defined as cases for which a diagnosis was reached on the MINI but not in the patient's chart. Results: Of the 840 primary care patients assessed, 27.2%, 11.4%, 12.6%, 31.2%, and 16.5% of patients met criteria for major depressive disorder, bipolar disorder, panic disorder, generalized anxiety disorder, and social anxiety disorder, respectively. Misdiagnosis rates reached 65.9% for major depressive disorder, 92.7% for bipolar disorder, 85.8% for panic disorder, 71.0% for generalized anxiety disorder, and 97.8% for social anxiety disorder. Conclusions: With high prevalence rates and poor detection, there is an obvious need to enhance diagnostic screening in the primary care setting. Prim Care Companion CNS Disord 2011;13(2):e1-e10 Submitted: May 11, 2010; accepted August 13, 2010. Published online: April 28, 2011 (doi:10.4088/PCC.10m01013). Corresponding author: Monica Vermani, PsyD, START Clinic for Mood and Anxiety Disorders, 32 Park Rd, Toronto, Ontario, M4W 2N4 (mvermani@startclinic.ca).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".