A CROSSWALK OF COMMONLY USED FRAILTY SCALES
Bibliographic record
Abstract
Abstract Several validated scales have been developed to measure frailty, yet it remains unknown how these measures are related. We used data from 7,070 community-dwelling older adults who participated in National Health and Aging Trend Study round 5 to construct a crosswalk among frailty measures. We operationalized the 60-item Frailty Index (FI), Study of Osteoporotic Fracture (SOF) Index, FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indicator (TFI), Groningen Frailty Indicator (GFI), and Edmonton Frailty Scale (EFS). Missing data, needed for the calculation of frailty scores, were imputed using multiple imputation by chained equations method. We then linked the scores of each frailty measure to FI using the equipercentile method, a statistical procedure that links different scales by equating percentile distributions. Participants considered frail on FI (cutpoint of 0.25) corresponded to the following scores on each frailty measure: SOF 1.3, FRAIL 1.7, Phenotype 1.7, CFS 5.3, VES-13 5.5, TFI 4.4, GFI 4.4, and EFS 5.8. Conversely, individuals considered frail on each frailty measure corresponded to the following FI scores: 0.37 (SOF), 0.40 (FRAIL), 0.42 (Phenotype), 0.21 (CFS), 0.19 (VES-13), 0.28 (TFI), 0.22 (GFI), and 0.37 (EFS). The CFS, VES-13, TFI and GFI each discriminates between non-frail and frail people in the pre- to mildly frail spectrum on the FI, whereas the SOF, FRAIL Scale, Phenotype, and EFS detect those in the higher frailty spectrum on the FI. Our results provide clinicians and researchers with a useful tool to convert and interpret frailty across scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".