Association between frailty index and mortality in depressed patients: results from NHANES 2005–2018
Bibliographic record
Abstract
This study investigated the relationship between the frailty index and all-cause and cause-specific mortality in patients with depression. We recruited 2,669 participants with depression from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 and quantified their frailty status using a 53-item frailty index. Cox proportional hazards models were used to estimate hazard ratios (HR) and their 95% confidence intervals (CI). The median (IQR) frailty score was 0.3 (0.2, 0.4). During a median follow-up of 7.1 years, 342 all-cause deaths (including 85 cardiovascular deaths and 70 cancer deaths) were recorded. Compared to the lowest frailty index tertile, the adjusted HR (95% CI) for all-cause mortality in the highest tertile was 2.91 (1.97, 4.3), for cardiovascular mortality was 3.13 (1.37, 7.19), and for cancer mortality was 2.3 (1.05, 5.03). Each unit increase in the frailty index (log-transformed) was associated with a 241% increase in all-cause mortality (P < 0.001), a 233% increase in cardiovascular mortality (P < 0.001), and a 185% increase in cancer mortality (P < 0.001). These results were consistent across analyses stratified by age, gender, race, BMI, hypertension, and diabetes. This study suggests that the frailty index is positively associated with all-cause and cause-specific mortality in patients with depression. The frailty index could serve as a prognostic indicator, and frailty interventions should be an important part of managing patients with depression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".