Frailty change based on minimally important difference in nursing home residents: FIRST cohort study findings.
Bibliographic record
Abstract
BACKGROUND: Frailty is common among residential aged care services (RACS) residents; however, little is known about how frailty changes over time in this population. This study aimed to estimate minimally important difference (MID) in frailty to then describe: frailty change over 12 months; and factors associated with worsening frailty. METHODS: Prospective cohort study across 12 RACS sites of a single aged care organisation in South Australia (n = 548 residents, mean age 87.7 ± 7.2 years, 72.6% female). Frailty was measured using a frailty index (FI) with 12 months between baseline and follow-up. MID was calculated cross-sectionally (anchor-based using self-reported health, and ½SD for distribution-based). RESULTS: Between-person MID for the FI was identified as 0.037 (anchor-based) and 0.063 (distribution-based). Using the conservative value of 0.063 as the basis for change, 32.3% (n = 177) of residents remained stable, 13.7% (n = 75) improved, 33.0% (n = 181) worsened and 21.0% (n = 115) died over 12 months. In a multivariable analysis, significant predictors of the dichotomous outcome of worsening and death at 12 months were: being malnourished (odds ratio (OR) = 2.15, 95% confidence interval (CI) = 1.23, 3.75), at risk of malnutrition (OR = 1.98, 95%CI = 1.34, 2.91) and diabetes (OR = 1.61, 95%CI = 1.06, 2.42) compared to those who remained stable or improved. CONCLUSIONS: A 6.3% change in frailty for RACS residents is a conservative MID. Frailty is dynamic in RACS residents, and stability or improvement was possible even for the most-frail. Treatments such as nutritional interventions, exercise and diabetes management are likely to benefit frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".