FRAILTY IN AUSTRALIAN RESIDENTIAL AGED CARE FACILITIES: RELATIONSHIP WITH ONE-YEAR OUTCOMES
Bibliographic record
Abstract
This study aimed to investigate the ability of the FRAIL-NH and Frailty Index to predict hospitalization and mortality in residential age care facilities over a 12-month follow up. A total of 383 residents (age 87.5 ± 6.2 years, 77.5% females) of six Australian facilities participated in the study. At baseline 35.9% of residents were classified as most frail based on the 7-item FRAIL-NH scale. Their median 66-item Frailty Index score was 0.33 (IQR 0.24–0.46). During the follow up period, 22.2% of residents died and 34.4% were hospitalized. Residents who died had higher FRAIL-NH (45.2% vs. 35.2% most frail) and Frailty Index [median (IQR) 0.41 (0.29–0.53) vs. 0.31 (0.23–0.42)] scores at baseline. Residents who were hospitalized had lower FRAIL-NH (25.2% vs. 41% most frail) and Frailty Index [median (IQR) 0.31 (0.24–0.41) vs. 0.35 (0.24–0.48)] scores at baseline. The FRAIL-NH scale and Frailty Index may help identify residents most vulnerable to hospitalization and mortality.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".