DEFINING MINIMAL IMPORTANT DIFFERENCES AND ESTABLISHING CATEGORIES FOR THE FRAILTY INDEX
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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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 | 0.036 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".