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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".