Can laboratory test-based frailty indices contribute to frailty screening in emergency departments?
Bibliographic record
Abstract
BACKGROUND: Laboratory-based frailty indices (FI-Labs) offer potential adjuncts and alternatives to clinical assessments. Still, their optimal configuration and construct validity compared with nurse-assessed Clinical Frailty Scale (CFS) scores remain unclear. METHODS: In this retrospective cohort study, we evaluated five FI-Lab configurations against nurse-assessed CFS scores using data from 74 493 emergency department visits. We examined their association with clinical outcomes and assessed measurement reliability using mixed effects models. RESULTS: While nurse assessments demonstrated superior outcome discrimination (c-statistic 0.726 for 90-day mortality versus 0.718 for best FI-Lab), automated FI-Lab measures showed significantly greater between-visit reliability [intraclass correlation coefficient (ICC) = 0.51-0.76 versus 0.37 for nurse CFS]. The drug-adjusted FI-Lab demonstrated highest reliability (ICC = 0.76) but weaker age associations (β = 0.002, P = .08) compared to other configurations (β = 0.006-0.013, P < .001). In complex models adjusting for illness severity, nurse CFS scores showed stronger mortality associations (HR 1.55, 95% CI 1.45-1.66 per standard deviation) compared to FI-Lab configurations (HR range 1.19-1.29). Notably, all frailty measures showed effect sizes comparable to age (HR range 1.37-1.55 per SD). CONCLUSIONS: Automated FI-Lab measures offer a reliability advantage over nurse-assessed CFS scores despite slightly lower predictive validity for mortality. Their comparable effect sizes to age suggest these automated measures capture clinically meaningful patient characteristics. This trade-off between reliability and predictive validity suggests that integrated approaches combining automated screening with targeted clinical assessment may provide optimal frailty identification in emergency settings.
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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".