Applicability of laboratory deficit‐based frailty index in predominantly older patients with end‐stage renal disease under chronic dialysis: A pilot test of its correlation with survival and self‐reported instruments
Bibliographic record
Abstract
ABSTRACT Aim Laboratory deficit‐based frailty index (LFI) exhibited outcome‐prediction ability in the elderly, but not in those with end‐stage renal disease (ESRD). We hypothesized that LFI results might have outcome correlation and correlate closely with other instruments in ESRD patients. Methods We prospectively enroled ESRD patients between 2014 and 2015 and administered self‐report frailty instruments (Strawbridge questionnaire, Edmonton frail scale (EFS), Groningen frailty indicator (GFI), Tilburg frailty indicator, G8 questionnaire and FRAIL scale), and Cardiovascular Health Study (CHS) scale, with two types of LFI calculated. They were followed up until June 30, 2017. Correlations between the results of six instruments, CHS scale, and those of LFI were identified, followed by Kaplan–Meier survival analyses and logistic regression analyses to compare those with high and low LFI. Results The frailty prevalence was 33.3% (CHS), 78.8% Strawbridge questionnaire, 45.5% (EFS), 57.6% (GFI), 27.3% (Tilburg frailty indicator), 84.8% (G8) and 18.2% (FRAIL) among ESRD participants. LFI‐1 results were significantly correlated with those of LFI‐2 (P < 0.01), EFS (P = 0.04) and GFI (P < 0.01), while LFI‐2 results were not. Those with CHS or GFI‐identified frailty had significantly lower 1,25‐(OH)2‐D levels than those without. After 32.3 ± 5.4 months, patients with high LFI‐1 scores, but not LFI‐2, had a significantly higher mortality than those with lower scores. GFI and EFS scores were also independently associated with LFI‐1, while CHS scores exhibited borderline association only. Conclusion Among a group of predominantly older ESRD patients, LFI differentiates patients with good and poor outcomes, supporting its applicability in these patients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".