DEFINING MINIMAL IMPORTANT DIFFERENCES AND ESTABLISHING CATEGORIES FOR THE FRAILTY INDEX
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We aimed to define minimal clinically important differences (MID) in the Frailty Index (FI) and to establish FI categories (FIc) in two clinical and three population cohorts. Data came from the Survey of Health, Ageing, and Retirement in Europe (SHARE: n = 29851, median age in years [range]: 63.0 [50.0–104.0]), the Canadian Study of Health and Ageing (n = 5516, 80.0 [70.0–104.0], the National Health and Nutrition Examination Survey [n = 3146, 66.0 [50.0–85.0], the Older Patient Information Database [n = 912, 81.0 [56.0–103.0], and the Canadian Immunization Research Network Serious Outcomes Surveillance Network (SOS: n = 6063, 80.0 [65.0–105.0]). FIs were constructed using the deficit accumulation approach. MIDs were defined by Cohen’s effect sizes and bootstrapping analysis. The FIc were determined by Clinical Frailty Scale (CFS) levels and validated by stratum-specific likelihood ratios (SSLRs) against adverse health outcomes. The most conservative MID in the FI across the cohorts was 0.03 [95% CI: 0.03, 0.03]. Results remained similar when stratified by age and sex. The FIc identified based on the CFS was <0.20, 0.20–0.30, 0.30–0.40, >0.40. The FIc displayed a dose-response relationship with ≥2 weeks of hospitalization (e.g. SHARE SSLRs: 0.491 [95% CI: 0.448, 0.536], 1.017 [0.908, 1.154], 1.746 [1.472, 2.035], 2.620 [2.276, 3.051]) and mortality (e.g. SOS SSLRs: 0.500 [95% CI: 0.442, 0.556], 0.924 [0.819, 1.033], 1.733 [1.486, 1.980], 3.264 [2.825, 3.722]). Identifying the MID in the FI and establishing the FIc can assist with using frailty as an outcome in interventional studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it