Comparison of Claims-Based Definitions vs. Measured Frailty in Patients with CKD
Bibliographic record
Abstract
Background: Frailty is common in patients with Chronic Kidney Disease (CKD), and those affected by both conditions are at increased risk of adverse outcomes including worsened disability, hospitalization, and death. Collecting data on frailty status as part of routine clinical care could enhance care by identifying patients at high risk of adverse events. Clinical assessment of frailty is time and resource intensive. Frailty definitions based on administrative data might provide a feasible and efficient alternative. The primary objective of this study was to compare agreements between administrative claims-based definitions for frailty versus objectively measured frailty in adults with advanced (Stage G4+), non-dialysis CKD. Methods: The cohort consisted of Manitoba participants from the Canadian Frailty Observation and Interventions Trial (CanFIT). This multicentre cohort study followed 442 adults with an eGFR < 30mL/min/1.73 m2 longitudinally. At each visit, an assessment was conducted to determine frailty status using the Fried Frailty Index, Short Physical Performance Battery, and healthcare providers impression. The CanFIT database was linked to several administrative health databases at the Manitoba Centre for Healthy Policy to calculate two claims-based frailty indices, the Segal Frailty Index and the Pre-operative Frailty Index, which have been previously validated in the non-CKD literature. Results: Of participants included, the mean age was 65.8±13.9 years and 58.4% were male; 87.8% had hypertension, 61.3% dyslipidemia, and 57.5% diabetes. The prevalence of frailty varied from 18.1% to 69.5% depending on definition. Agreement between claims-based frailty indices and objective and subjective measures of frailty was low to modest (κ 0.08–0.31). Individuals considered frail, using both administrative or measured definitions, had an increased risk of all-cause mortality and hospitalization. Conclusion: This study suggests that claims-based definitions of frailty developed in the general population are poor substitutes for identifying frailty in individuals with advanced, non-dialysis CKD. Efforts to integrate valid and efficient frailty assessments in clinical practice are needed to improve clinical decision making.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".