Antidepressant and antipsychotic prescribing in primary care for people with dementia.
Bibliographic record
Abstract
OBJECTIVE: To use data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to evaluate the prevalence of antidepressant and antipsychotic prescriptions among patients with no previous depression or psychosis diagnoses, and to identify the factors associated with the use of these drugs in this population. DESIGN: Retrospective cohort study using data derived from CPCSSN. SETTING: Primary care practices associated with CPCSSN. PARTICIPANTS: Patients who were born before 1949; who were associated with a CPCSSN primary care practitioner between October 1, 2007, and September 30, 2013; and whose electronic medical records contained data from at least 6 months before and 12 months after the date of dementia diagnosis. MAIN OUTCOME MEASURES: Prescription for an antidepressant or antipsychotic medication in the absence of a depression or psychosis diagnosis. Multivariable models were fitted to determine estimated odds ratios (ORs) and were adjusted for age and sex. RESULTS: =.051). CONCLUSION: A substantial number of patients with dementia are being prescribed antidepressant or antipsychotic medications by their primary care practitioners without evidence of depression or psychosis in their electronic medical records.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".