Complementing chronic frailty assessment at hospital admission with an electronic frailty index (FI-Laboratory) comprising routine blood test results
Bibliographic record
Abstract
BACKGROUND: Acutely ill and frail older adults have complex social and health care needs. It is important to understand how this complexity affects acute outcomes for admission to hospital. We validated a frailty index using routine admission laboratory tests with outcomes after patients were admitted to hospital. METHODS: In a prospective cohort of older adults admitted to a large tertiary hospital in the United Kingdom, we created a frailty index from routine admission laboratory investigations (FI-Laboratory) linked to data comprising hospital outcomes. We evaluated the association between the FI-Laboratory and total days spent in hospital, discharge to a higher level of care, readmission and mortality. RESULTS: = 0.09). An increase in the CFS and the equivalent of 3 additional abnormal laboratory test results in the FI-Laboratory, respectively, were associated with an increased proportion of inpatient days (rate ratios [RRs] 1.43, 95% confidence interval [CI] 1.35-1.52; and 1.47, 95% CI 1.41-1.54), discharge to a higher level of care (odd ratios [ORs] 1.39, 95% CI 1.27-1.52; and 1.30, 95% CI 1.16-1.47) and increased readmission rate (hazard ratios [HRs] 1.26, 95% CI 1.17-1.37; and 1.18, 95% CI 1.11-1.26). Increases in the CFS and FI-Laboratory were associated with increased mortality HRs of 1.39 (95% CI 1.28-1.51) and 1.45 (95% CI 1.37-1.54), respectively. INTERPRETATION: We determined that FI-Laboratory, distinct from baseline frailty, could be used to predict risk of many adverse outcomes. The score is therefore a useful way to quantify the degree of acute illness in frail older adults.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".