Validation of a Frailty Ladder Using Rasch Analysis: If the Shoe Fits
Bibliographic record
Abstract
Abstract The current measurement approach to frailty is to classify people on frailty status, rather than measure the degree to which they are frail. Here, we test the extent to which a set of items identified within the frailty concept fits a hierarchical linear model (Rasch model) and form a true measure reflective of the frailty construct and confirm the model using the NuAge dataset. The development sample included 234 individuals (aged 57 to 97) drawn from three sources: at-risk seniors (n=141); post-colorectal surgery (n=47); and post-rehabilitation hip fracture (n=46). We defined our frailty construct based on items commonly used in frailty indices, self-report measures, and performance tests. Of the 68 items, 29 fit the Rasch Model: 19 self-report items on physical function and 10 performance tests including one for cognition. Items typically identified as reflecting the frailty concept fit the Rasch model. The Frailty Ladder would facilitate personalized intervention.
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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 | 0.035 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".