1995 Constructing a Frailty Index using routinely collected measures to study its relationship with adverse health outcomes
Bibliographic record
Abstract
Abstract Introduction Any Frailty Index (FI) measures overall health. The FI-Lab employs common laboratory data and clinical measures to do so. Objective To examine how an FI-lab constructed from vital signs, laboratory tests, and electrocardiographic data is associated with in-patient admission and time to death. FI-Lab performance was compared with an FI from a Comprehensive Geriatric Assessment (FI-CGA), the Clinical Frailty Scale (CFS), and the Canadian Triage Acuity Scale (CTAS). Method Participants were Emergency Department (ED) patients aged 65+ years referred to Internal Medicine, staffed by a geriatrician (KR). Fifty-seven FI-Lab variables were binarized (0 = no deficit; 1 = deficit) using standard normal ranges. Each FI was calculated as the fraction of items present as deficits. Age- and sex-adjusted Cox proportional hazard and logistic regression models were used to assess relationships with all-cause mortality, and in-patient admission, respectively. Results Of 808 patients, an FI-Lab was calculable in 807. Median age was 81 years (IQR:13); 55.7% were female. FI-Lab values ranged from 0.05–0.78 (Mean: 0.51; Standard deviation (SD) 0.10). Females (0.50±0.11) had lower FI-Lab scores than males (Mean: 0.52±0.09; p=0.003). At 30 days, each 0.01 FI-Lab unit increase showed higher mortality Hazard Rate (HR) (95% Confidence Interval (CI):1.04 (1.02–1.06) and inpatient admission risk: Odds ratio (OR) 1.02 (1.00–1.03), as did the FI-CGA (1.04; 1.02-1.04) and CTAS (1.46; 1.02-2.10). Similar results held for inpatient admission, save for CTAS (0.95; 0.54-1.64). By two years, only the FI-lab and CFS significantly predicted mortality risk. Conclusion FI-Lab scores were associated with higher mortality rates and in-patient admission risk in older ED patients referred to Medicine. In acute care, the FI-Lab appears to integrate baseline frailty with illness severity. As such data often are routinely available, the FI-Lab might be an additional passive measure of frailty-related risk, potentially available in real time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".