Depression in patients with advanced illness: An examination of Ontario complex continuing care using the Minimum Data Set 2.0
Bibliographic record
Abstract
OBJECTIVE: In this study, we examined the prevalence of depression, its recognition, and its treatment in continuing care patients with advanced illness (AI). METHODS: All data were obtained from the Ontario (Canada) provincially-mandated MDS 2.0 form for chronic care. Of 3,801 patients, 524 met our empiric definition of AI, which was predicated on a previously validated algorithm. The MDS-embedded Depression Rating Scale (DRS) was used to measure psychological well-being and a score of 3 or greater indicated potential depression. RESULTS: Twenty-nine percent of patients with AI scored greater than 3, making them nearly twice as likely to be potentially depressed as other patients (OR 1.8, 95% CI 1.5-2.2). Despite this patients with AI were less likely to have received antidepressants (28.9% vs. 38.2%), even among those with a diagnosis (45.3% vs. 58.4%). Using logistic regression, correlates of potential depression were identified and surprisingly patients with cancer were substantially less likely to be depressed (AOR 0.37, 95% CI 0.2-0.6). Further investigation revealed that cancer patients were more likely to be treated for depression and to be recognized as being within the terminal phase of illness. SIGNIFICANCE OF RESULTS: These findings suggest that a high proportion of terminally ill patients had unmet needs for psychological support. As well, they suggest that cancer patients received better targeted end-of-life care, which resulted in an overall decrease in psychological distress when compared to other patients with similarly advanced illness.
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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 | 0.000 | 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, 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".