Development of a Frailty Ladder Using Rasch Analysis: If the Shoe Fits
Bibliographic record
Abstract
Background: The current measurement approach to frailty is to create an index of frailty status, rather than measure it. The purpose of this study is to test the extent to which a set of items identified within the frailty concept fit a hierarchical linear model (e.g., Rasch model) and form a true measure reflective of the frailty construct. Methods: A sample was assembled from three sources: community organization for at-risk seniors (n=141); colorectal surgery group assessed post-surgery (n=47); and hip fracture assessed post-rehabilitation (n=46). The 234 individuals (age 57 to 97) contributed 348 measurements. The frailty construct was defined according to the named domains within commonly used frailty indices, and items drawn to reflect the frailty came from self-report measures. Performance tests were tested for the extent to which they fit the Rasch model. Results: Of the 68 items, 29 fit the Rasch model: 19 self-report items on physical function and 10 performance tests, including one for cognition; patient reports of pain, fatigue, mood, and health did not fit; nor did body mass index (BMI) nor any item representing participation. Conclusion: Items that are typically identified as reflecting the frailty concept fit the Rasch model. The Frailty Ladder would be an efficient and statistically robust way of combining results of different tests into one outcome measure. It would also be a way of identifying which outcomes to target in a personalized intervention. The rungs of the ladder, the hierarchy, could be used to guide treatment goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".