Depression and use of health care services in patients with advanced cancer.
Bibliographic record
Abstract
OBJECTIVE: To examine whether depression in patients with advanced cancer is associated with increased rates of physician visits, especially to primary care. DESIGN: Retrospective, observational study linking depression survey data to provincial health administration data. SETTING: Toronto, Ont. PARTICIPANTS: A total of 737 patients with advanced cancer attending Princess Margaret Hospital, who participated in the Will to Live Study from 2002 to 2008. MAIN OUTCOME MEASURES: Frequency of visits to primary care, oncology, surgery, and psychiatry services, before and after the depression assessment. RESULTS: Before the assessment, depression was associated with an almost 25% increase in the rate of primary care visits for reasons not related to mental health (rate ratio [RR] = 1.23, 95% CI 1.00 to 1.50), adjusting for medical morbidity and other factors. After assessment, depression was associated with a 2-fold increase in the rate of primary care visits for mental health-related reasons (RR = 2.35, 95% CI 1.18 to 4.66). However, depression was also associated during this time with an almost 25% reduction in the rate of oncology visits (RR = 0.78, 95% CI 0.65 to 0.94). CONCLUSION: Depression affects health care service use in patients with advanced cancer. Individuals with depression were more likely to see primary care physicians but less likely to see oncologists, compared with individuals without depression. However, the frequent association of disease-related factors with depression in patients with advanced cancer highlights the need for communication between oncologists and primary care physicians about the medical and psychosocial care of these patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".