Validation of Mood and Anxiety Disorder Case Definitions using Primary Care Electronics Medical Records
Bibliographic record
Abstract
Context: Mental health conditions have increasing prevalence, co-occurrence, and high management burden within primary care settings. Objective: To validate and apply electronic medical record (EMR)-based definitions for mood and anxiety disorders (inc. depression, anxiety, bipolar disorder), and schizophrenia. Study Design: Retrospective cross-sectional study. Setting: De-identified EMR data from 1,574 primary care providers participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Population: 1,692,987 patients from seven Canadian provinces with a visit between January 1, 2011, and December 31, 2021. Intervention/Instrument: The reference set included 2,488 randomly selected patients, including 434 (17.4%) positive cases (i.e. depression n=249, anxiety n=261, bipolar disorder n=19, schizophrenia n = 6) and 2,054 (82.6%) negatives. A second reference set for schizophrenia was created that included 760 patients (30 [3.9%] positive and 730 [96.1%] negative). Outcome Measures: We assessed agreement between 29 case definitions and the reference set using the following metrics sensitivity (sen), specificity (spec), positive predictive value (PPV), negative predictive value (NPV). Prevalence and 95% confidence limits were computed using exact binomial test. Exploratory analysis assessed co-occurrence of conditions. Results: Definition 11 captured anxiety, depression, and bi-polar diagnoses with sen 80.7, spec 88.7, PPV 59.9, and NPV 95.7 and an estimated prevalence of 21.8% (21.7-21.9). When validated separately depression produced moderate agreement (sen 79.9, spec 94.2, PPV 60.5, NPV 97.7), whereas anxiety and bipolar disorder had notably lower agreement (anxiety: sen 53.6, spec 87.9, PPV 34.2, NPV 94.2; bipolar: sen 89.5, spec 98.3, PPV 28.8, NPV 99.9). The inclusion of psychosis in mood and anxiety definitions did not improve agreement (sen 95.2, spec, 80.7, PPV, 51.0), however alone schizophrenia had high agreement (sen 93.3, spec 100, PPV 100, NPV 99.9). There was high co-occurrence of anxiety, depression and bipolar disorder with the majority of patients diagnosed with ≥2 conditions. Conclusions: We found high co-occurrence of anxiety, depression and bipolar disorder. Algorithms to capture these conditions together produced stronger agreement compared to individual definitions. Application of validated algorithms to capture mental health conditions can inform disease surveillance and health system planning.
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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 | 0.051 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".