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 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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".