Comparing the Fried frailty phenotype versus the Veterans Affairs frailty index among dialysis dependent patients
Bibliographic record
Abstract
INTRODUCTION: Frailty in dialysis patients is a modifiable disease state which can increase mortality if left untreated but remains underdiagnosed as frailty evaluations can be arduous or time consuming. We evaluate the agreement between a clinical frailty construct (Fried frailty phenotype, FFP) against and an electronic health record-based Veterans Affairs Frailty Index (VAFI) and their association with mortality. METHODS: A retrospective cohort analysis of 764 participants from the ACTIVE/ADIPOSE study was performed. Frailty as measured by VAFI and FFP was obtained and Kappa statistic estimating concordance between the two scores were calculated. Differences in mortality risk were analyzed according to presence or absence of frailty. FINDINGS: When assessing agreement between the VAFI and FFP, the kappa statistic was 0.09 (95% confidence interval [CI] 0.02-0.16) suggesting a low level of agreement. Frailty was independently associated with higher mortality risk (hazards ratio [HR] 1.40-1.42 in fully adjusted models depending upon frailty construct). Discordantly frail patients by construct had a higher risk of mortality though this was not statistically significant after adjustment. However, concordantly frail patients had much higher mortality risk compared to concordantly nonfrail (adjusted HR 2.08, 95% CI 1.44-3.01). DISCUSSION: Poor agreement between constructs is likely reflective of the multifactorial definition of frailty. While further longitudinal studies are needed to determine if the VAFI would be beneficial in the reassessment of frailty, it may be beneficial as a cue for further frailty testing (e.g., with FFP) with the combination of multiple frail constructs providing improved prognostic information.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".