Using Canadian Primary Care Sentinel Surveillance Network data to examine depression in patients with a diagnosis of Parkinson disease: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Parkinson disease is a complex neurodegenerative disorder, and a comorbidity of depression is common. We aimed to describe demographic and health characteristics of patients with Parkinson disease and examine sex differences in antidepressant prescriptions for those with comorbid depression using electronic medical records. METHODS: We analyzed Canadian Primary Care Sentinel Surveillance Network data for patients 18 years and older with a diagnosis of Parkinson disease who had at least 1 primary care encounter between Sep. 30, 2012, and Sep. 30, 2014. We used regression modelling to determine sex differences in antidepressant prescriptions. An advisory group of clinicians helped determine the common list of medications and interpreted the results. RESULTS: We identified a total of 1815 patients (54.9% male) with Parkinson disease during the study period. The mean age of patients was 74.6 years. Most (82.0%) lived in urban areas. Patients had a mean number of 15.5 primary care encouters over the 2-year study period. Almost 40% of patients had a concurrent diagnosis of depression. More than half of the patients had received a depression diagnosis within 1 year of their Parkinson diagnosis. Eight out of every 10 patients had a prescription for at least 1 medication for depression, the most frequently prescribed being selective serotonin reuptake inhibitors (SSRIs). No sex differences were found in the number or type of medications. INTERPRETATION: Our findings support Canadian Parkinson Guidelines for Routine Screening of Comorbid Depression, but more evidence and decision-support tools are needed to examine the efficacy of antidepressants and assist clinicians in evaluating the frequent SSRI prescriptions in this population.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".