Agreement between primary care and hospital diagnosis of schizophrenia and bipolar disorder: A cross-sectional, observational study using record linkage
Bibliographic record
Abstract
People with serious mental illness die 10-25 years sooner than people without these conditions. Multiple challenges to accessing and benefitting from healthcare have been identified amongst this population, including a lack of coordination between mental health services and general health services. It has been identified in other conditions such as diabetes that accurate documentation of diagnosis in the primary care chart is associated with better quality of care. It is suspected that if a patient admitted to the hospital with serious mental illness is then discharged without adequate identification of their diagnosis in the primary care setting, follow up (such as medication management and care coordination) may be more difficult. We identified cohorts of patients with schizophrenia and bipolar disorder who accessed care through the North York Family Health Team (a group of 77 family physicians in Toronto, Canada) and North York General Hospital (a large community hospital) between January 1, 2012 and December 31, 2014. We identified whether labeling for these conditions was concordant between the two settings and explored predictors of concordant labeling. This was a retrospective cross-sectional study using de-identified data from the Health Databank Collaborative, a linked primary care-hospital database. We identified 168 patients with schizophrenia and 370 patients with bipolar disorder. Overall diagnostic concordance between primary care and hospital records was 23.2% for schizophrenia and 15.7% for bipolar disorder. Concordance was higher for those with multiple (2+) inpatient visits (for schizophrenia: OR 2.42; 95% CI 0.64-9.20 and for bipolar disorder: OR 8.38; 95% CI 3.16-22.22). Capture-recapture modeling estimated that 37.4% of patients with schizophrenia (95% CI 20.7-54.1) and 39.6% with bipolar disorder (95% CI 25.7-53.6) had missing labels in both settings when adjusting for patients' age, sex, income quintiles and co-morbidities. In this sample of patients accessing care at a large family health team and community hospital, concordance of diagnostic information about serious mental illness was low. Interventions should be developed to improve diagnosis and continuity of care across multiple settings.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".