Quality of life in dialysis: A <scp>M</scp>alaysian perspective
Bibliographic record
Abstract
There is a growing interest to use quality of life as one of the dialysis outcome measurement. Based on the Malaysian National Renal Registry data on 15 participating sites, 1569 adult subjects who were alive at December 31, 2012, aged 18 years old and above were screened. Demographic and medical data of 1332 eligible subjects were collected during the administration of the short form of World Health Organization Quality of Life questionnaire (WHOQOL-BREF) in Malay, English, and Chinese language, respectively. The primary objective is to evaluate the quality of life among dialysis patients using WHOQOL-BREF. The secondary objective is to examine significant factors that affect quality of life score. Mean (SD) transformed quality of life scores were 56.2 (15.8), 59.8 (16.8), 58.2 (18.5), 59.5 (14.6), 61.0 (18.5) for (1) physical, (2) psychological, (3) social relations, (4) environment domains, and (5) combined overall quality of life and general health, respectively. Peritoneal dialysis group scored significantly higher than hemodialysis group in the mean combined overall quality of life and general health score (63.0 vs. 60.0, P < 0.001). Independent factors that were associated significantly with quality of life score in different domains include gender, body mass index, religion, education, marital status, occupation, income, mode of dialysis, hemoglobin, diabetes mellitus, coronary heart disease, cerebral vascular accident and leg amputation. Subjects on peritoneal dialysis modality achieved higher combined overall quality of life and general health score than those on hemodialysis. Religion and cerebral vascular accident were significantly associated with all domains and combined overall quality of life and general health.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".