Exploring Clinically Meaningful Changes for the Frailty Index in a Longitudinal Cohort of Hospitalized Older Patients
Bibliographic record
Abstract
BACKGROUND: Clinically meaningful change (CMC) for frailty index (FI) scores is little studied. We estimated the CMC by associating changes in FI scores with changes in the Clinical Frailty Scale (CFS) in hospitalized patients. METHODS: The Serious Outcomes Surveillance Network of the Canadian Immunization Research Network enrolled older adults (65+ years) admitted to hospital with acute respiratory illness (mean age = 79.6 ± 8.4 years; 52.7% female). Patients were assigned CFS and 39-item FI scores in-person at admission and via telephone at 1-month postdischarge. Baseline frailty state was assessed at admission using health status 2 weeks before admission. We classified those whose CFS scores remained unchanged (n = 1,534) or increased (n = 4,390) from baseline to hospital admission, and whose CFS scores remained unchanged (n = 1,565) or decreased (n = 2,546) from admission to postdischarge. For each group, the CMC was represented as the FI score change value that best predicted one level CFS change, having the largest Youden J value in comparison to no change. RESULTS: From baseline to admission, 74.1% increased CFS by ≥1 level. From admission to postdischarge, 61.9% decreased CFS by ≥1 levels. A change in FI score of 0.03 best predicted both one-level CFS increase (sensitivity = 70%; specificity = 69%) and decrease (sensitivity = 66%; specificity = 61%) in comparison to no change. Of those who changed CFS by ≥1 levels, 70.9% (baseline to admission) and 72.4% (admission to postdischarge) changed their FI score by at least 0.03. CONCLUSIONS: A clinically meaningful change of 0.03 in the frailty index score holds promise as a benchmark for assessing the meaningfulness of frailty interventions.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".