Short Physical Performance Battery as a Crosswalk Between Frailty Phenotype and Deficit Accumulation Frailty Index
Bibliographic record
Abstract
BACKGROUND: Growing evidence supports the clinical importance of evaluating frailty in older adults, with its strong outcome relevance. We aimed to assess whether the Short Physical Performance Battery (SPPB) correlates with frailty status according to phenotype and deficit accumulation models and can be used as a link between these models. METHODS: We analyzed records of 1064 individuals from the Aging Study of Pyeongchang Rural Area, a population-based, prospective cohort from South Korea. Frailty was determined using the Cardiovascular Health Study (CHS) phenotype (phenotype model), 26- and 34-item frailty indices (deficit accumulation model). Associations of SPPB score and frailty with a composite outcome of mortality or long-term institutionalization were assessed. Crosswalks for SPPB, the CHS frailty phenotype, and the frailty index were created. RESULTS: The mean age of the study population was 76.0 years, and 583 (54.8%) were women. According to the CHS phenotype, 26- and 34-item frailty indices, 242 (22.7%), 161 (15.1%), and 280 (26.3%) participants, respectively, had frailty. Sensitivities/specificities for classifying CHS phenotype, 26- and 34-item frailty indices were 0.93/0.55, 0.71/0.84, and 0.80/0.83 by SPPB cut points of ≤9, ≤6, and ≤7, respectively. C-index of SPPB score (0.78) showed a predictive ability for the composite outcome that was comparable to that of CHS frailty phenotype (0.79), 26- (0.78), and 34-item frailty indices (0.79). CONCLUSIONS: We could create a crosswalk linking frailty phenotype and frailty index from correlations between SPPB and frailty models. This result may facilitate clinical adoption of the frailty concept in a broader spectrum of older adults.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".