A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes
Bibliographic record
Abstract
BACKGROUND: abnormal laboratory test results accumulate with age and can be common in people with few clinically detectable health deficits. A frailty index (FI) based entirely on common physiological and laboratory tests (FI-Lab) might offer pragmatic and scientific advantages compared with a clinical FI (FI-Clin). OBJECTIVES: to compare the FI-Lab with the FI-Clin and to assess their individual and combined relationships with mortality and other adverse health outcomes. DESIGN AND SUBJECTS: secondary analysis of the eight-centre, longitudinal European Male Ageing Study (EMAS) of community-dwelling men aged 40-79 at baseline. Follow-up assessment occurred 4.4 ± 0.3 (mean ± SD) years later. METHODS: we constructed a 23-item FI using common laboratory tests, blood pressure and pulse (FI-Lab), compared it with a previously validated 39-item FI using self-report and performance-based measures (FI-Clin) and finally combined both FIs to create a 62-item FI-Combined. Outcomes were all-cause mortality, institutionalisation, doctor visits, medication use, self-reported health, falls and fractures. RESULTS: the mean FI-Lab score was 0.28 ± 0.11, the FI-Clin was 0.13 ± 0.11 and FI-Combined was 0.19 ± 0.09. Age-adjusted models demonstrated that each FI was associated with mortality [HR (CI) FI-Lab: 1.04 (1.03-1.06); FI-Clin: 1.05 (1.04-1.06); FI-Combined: 1.07 (1.06-1.09)], institutionalisation, doctor visits, medication use, self-reported health and falls. Combined in a model with FI-Clin, the FI-Lab remained independently associated with mortality, institutionalisation, doctor visits, medication use and self-reported health. CONCLUSIONS: the FI-Lab detected an increased risk of adverse health outcomes alone and in combination with a clinical FI; further evaluation of the feasibility of the FI-Lab as a frailty screening tool within hospital care settings is needed.
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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) | 0.000 | 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".