Pharmacologic Response to a Diagnosis of Late-Life Depression: A Population Study in Quebec
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of receiving psychoactive medication and receiving recommended first-line pharmacotherapy in individuals with newly diagnosed late-life depression. METHODS: We undertook a retrospective database cohort study of 5258 beneficiaries of the Quebec provincial health insurance plan between 1999 and 2002. Subjects were aged 65 to 84 years and diagnosed with depression by primary care physicians or psychiatrists between October 2000 and March 2001; they had no depression diagnosis in the previous year. We defined receipt of psychoactive medication as having a pharmacy claim in the year following the depression diagnosis. We determined receipt of recommended first-line pharmacotherapy from the first psychoactive medication dispensed following diagnosis and defined it accordingly; we defined first-line pharmacotherapy according to the 2001 Canadian Psychiatric Association guidelines. We used multivariate generalized estimating equations models to identify the determinants of the 2 outcomes. RESULTS: A total of 4421 (84.1%) patients received psychoactive medication following diagnosis; 2623 (59.3%) patients had not received antidepressants in the previous year. Of these, 1310 (49.9%) received recommended first-line pharmacotherapy. Independent predictors of receiving psychoactive medication were female sex, depression not otherwise specified (NOS), increasing comorbidity, and living in rural areas. Independent predictors of receiving recommended first-line pharmacotherapy were male sex, depression NOS, receiving medication in the month following diagnosis, and having the same physician diagnosing and treating the patient. CONCLUSION: Male sex and continuity of care predicted that patients had the recommended medication dispensed.
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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 | 0.002 |
| 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 | 0.000 |
| 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".