Antidepressant therapy in cancer patients: initiation and factors associated with treatment
Bibliographic record
Abstract
PURPOSE: We assessed the impact of a cancer diagnosis and its timing on antidepressant initiation. We also examined patterns of, and factors associated with, new antidepressant treatment in cancer patients. METHODS: We followed 61 067 antidepressant-naive Australian Government Department of Veterans' Affairs clients. We used multivariable Cox proportional hazards models with time-varying covariates to compare antidepressant initiation in clients with and without cancer and to assess how initiation varies with time from diagnosis, adjusting for sociodemographic characteristics, health service use and co-morbidities. RESULTS: 17.2% (995/5795) of cancer patients initiated antidepressants and, on average, was more likely to initiate treatment than non-cancer controls with similar characteristics (initiation rate 9/100 person-years, 95% confidence interval: 8.5-9.6 vs 6.6/100 person-years, 95% confidence interval: 6.5-6.7). The peak initiation period was 12 weeks before and 16 weeks after diagnosis; cancer patients were 42% more likely to commence therapy than non-cancer patients (adjusted hazard ratio = 1.4, 1.2 to 1.7). Cancer patients with co-morbid disease, dispensed opioids, corticosteroids or anxiolytics and to whom death was approaching were more likely to initiate treatment. Median duration of antidepressant therapy was 16 weeks. CONCLUSION: New antidepressant treatment is more common in cancer populations than in cancer-free populations. Treatment was most commonly initiated around diagnosis time, a period when cancer drug treatments also commence. The timing of peak antidepressant uptake suggests treatment may be for short-term adjustment reactions, better managed without drugs. Durations of treatment are shorter than recommended for depression.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".