FRAIL scale: Predictive validity and diagnostic test accuracy
Bibliographic record
Abstract
OBJECTIVE: To examine the predictive validity of the FRAIL scale for mortality, and diagnostic test accuracy (DTA) against the frailty phenotype (FP). MEASUREMENT: Frailty was measured in 846 community-dwelling adults (mean age 74.3 [SD 6.3] years, 54.8% female) using a modified FRAIL scale and modified FP. Mortality was matched to death records. RESULTS: The FRAIL scale demonstrated significant predictive validity for mortality up to 10 years (Frail adjHR: 2.60, P < .001). DTA findings were acceptable for specificity (86.8%) and Youden index (0.50), but not sensitivity (63.6%), or area under the receiver operator curve (auROC) (0.75). DTA estimates were more acceptable when a cut-point of ≥2 characteristics was used rather than ≥3 in the primary DTA analysis. CONCLUSION: The FRAIL scale is a valid predictor of mortality. DTA estimates depend on FRAIL scale cut-point used. This instrument is a potentially useful frailty screening tool.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".