Frailty prevalence in Aotearoa New Zealand haemodialysis patients and its association with hospitalisations.
Bibliographic record
Abstract
AIMS: To use two frailty tools to assess frailty prevalence in a cohort of Aotearoa New Zealand haemodialysis (HD) patients and determine factors associated with frailty and frailty's association with adverse health outcomes. METHODS: Frailty was measured using the Fried score and Edmonton Frail Scale (EFS) in HD patients dialysing at dependent or satellite clinic sites in Waitematā District Health Board, Auckland. Linear regression models were used to explore factors associated with frailty measurements. Logistic regression models were used to assess associations between frailty and mortality and hospitalisations. RESULTS: 138 participants. Mean (SD) age: 61.5 (13.5) years. 70 females (51%). 51 (37%) were frail by Fried score. 51 (37%) were frail by EFS (overlap of 32 participants). Age, marital status, smoking status and albumin were independently associated with both measures of frailty. Medication number was additionally associated with Fried score. Pacific ethnicity and Charlson Comorbidity Index were associated with EFS score. After adjusting for covariables, only Fried frailty was associated with hospitalisations at six months. CONCLUSIONS: Pacific ethnicity was independently associated with increased risk of EFS frailty. Fried frailty was associated with hospitalisations at six months. Given the paucity of literature on the New Zealand population, further work within these ethnic groups is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".