Assessment of frailty and quality of life and their correlation in the haemodialysis population at <scp>Palmerston North Hospital</scp>, <scp>New Zealand</scp>
Bibliographic record
Abstract
AIM: End-stage kidney disease (ESKD) is increasingly becoming a healthcare concern in New Zealand and haemodialysis remains the most common modality of treatment. Frailty and health-related quality of life (HRQOL) are established predictors of prognosis and have already been shown to be poor in the dialyzing population. Existing data show correlation between these measures in the ESKD population, however there is little evidence for those on haemodialysis specifically. Our study aimed to assess for a correlation between frailty and HRQOL in the haemodialysis population at Palmerston North Hospital, and to assess for any differences in frailty and HRQOL scores between indigenous Māori and non-Māori subgroups. METHODS: A cross-sectional study was conducted involving 93 in-centre haemodialysis patients from Palmerston North Hospital, New Zealand. Baseline demographic data was measured alongside frailty and HRQOL scores, which were measured using the Kidney Disease Quality of Life tool (KDQOL-36) and the Edmonton Frail Scale. RESULTS: A statistically significant negative correlation was observed between frailty and all aspects of HRQOL (p < .05), with the strongest correlation observed between frailty and the physical component (r = -.64, p = <.001). Independent samples t-test showed no statistically significant difference between scores for Māori and non-Māori in frailty (M = 7.4, SD = 3.3 vs. M = 6.8, SD = 3.2; t (91) = -0.92, p = .80), or HRQOL (p values > .05 in all components). CONCLUSION: A negative correlation was observed between frailty and HRQOL. This information can be beneficial in guiding discussions around treatment modality and for future patients and useful in enabling better predictions of prognosis. No statistically significant differences in frailty and HRQOL scores were observed between Māori and non-Māori groups, however the generalizability of this finding is limited due to the insufficient size of the study population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".