REPLY TO LETTER FROM ROZZINI AND COLLEAGUES ABOUT DEPRESSION IN OLDER MEDICAL INPATIENTS
Bibliographic record
Abstract
To the Editor: We read with interest the letter from Dr. Rozzini and colleagues, which reports a study of the prevalence, correlates, and 6-month mortality of major and minor depression in a sample of older medical inpatients. We have compared these results with those from our own study (1,2), and from other comparable studies (3–9). Their sample was of patients admitted to an acute geriatric unit whereas ours was from all medical units at two hospitals. Our sample appeared to be more severely ill (mean Acute Physiology Score of 2.9 vs 1.8) and had a higher 6-month mortality rate (21% vs 14.1%) (2). The prevalence of major depression in their sample was similar at 13.3% (vs 14.2% and 44.5% in the two hospitals in our study), whereas the prevalence of minor depression was much higher (41.0% vs 9.4% and 7.9% at our two hospitals) (1). In our study, a history of prior depression was an important modifier of the effect of depression on mortality (2). Among patients with no prior history, a depression diagnosis was associated with higher mortality, but this association disappeared in multivariate analyses, after adjustment for age and other covariates. However, among patients with a history of depression, major depression at hospital admission was associated with decreased mortality, even after adjustment for covariates. It would therefore be of interest to know whether the association between a diagnosis of major depression and lower mortality in the Rozzini data was found both in patients with and without a history of depression, and whether it persisted after adjustment for age, comorbidity, severity of illness, and other potential confounders.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".