Predictive Validity and Responsiveness of Patient-Reported and Performance-Based Measures of Function in the Boston RISE Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported and performance-based measures (PBMs) are commonly used to measure physical function in studies of older adults. Selection of appropriate measures to address specific research questions is complex and requires knowledge of relevant psychometric properties. The aim of this study was to examine the predictive validity for adverse outcomes and responsiveness of a widely used patient-reported measure, the Late-Life Function and Disability Instrument (LLFDI), compared with PBMs. METHODS: We analyzed 2 years of follow-up data from Boston RISE, a cohort study of 430 primary care patients aged ≥65 years. Logistic and linear regression models were used to examine predictive validity for adverse outcomes and effect size and minimal detectable change scores were computed to examine responsiveness. Performance-based functional measures included the Short Physical Performance Battery, 400-m walk, gait speed, and stair-climb power test. RESULTS: The LLFDI and PBMs showed high predictive validity for poor self-rated health, hospitalizations, and disability. The LLFDI function scale was the only measure that predicted falls. Absolute effect size estimates ranged from 0.54 to 0.64 for the LLFDI and from 0.34 to 0.63 for the PBMs. From baseline to 2 years, the percentage of participants with a change ≥ minimal detectable change was greatest for the LLFDI scales (46-59%) followed by the Short Physical Performance Battery (44%), gait speed (35%), 400-m walk (17%), and stair-climb power test (9%). CONCLUSIONS: The patient-reported LLFDI showed comparable psychometric properties to PBMs. Our findings support the use of the LLFDI as a primary outcome in gerontological research.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".