Survival and other clinical outcomes of maintenance hemodialysis patients in <scp>T</scp>aiwan: A 5‐year multicenter follow‐up study
Bibliographic record
Abstract
The increasing aging and diabetes mellitus (DM) patients in dialysis population make the quality maintenance of dialysis an imperative issue. Recently, an increasing number of dialysis centers were run by private dialysis providers, many of which apply quality assurance programs and performance management systems to dialysis care. We studied patients in dialysis facilities in Taiwan run by a private chain to see clinical outcomes of centers operating under these systemic strategies. Hemodialysis patients from January 1, 2008 to December 31, 2012 in 25 dialysis facilities in Taiwan, which received the management and consultation from a dialysis service provider, NephroCare (NC), were included. Data pivotal to quality of dialysis were analyzed. During a 5-year interval, 5161 hemodialysis patients were included. For volume control, the proportion of patients with weight gain ≥4.5% decreases from 41.7% to 30.2%. Mean Kt/V is 1.74 ± 0.28. Mean albumin level is 3.92 ± 0.38 g/dL. Patients with phosphate <5.5 mg/dL is up to 71.8%. The mean hemoglobin level is 10.70 ± 1.40 g/dL. More than 80% of patients have adequate iron status. Further, 73% of patients use native arteriovenous fistula. Hospitalization-free survival rate was 56% at the fifth year. Patient survival rate at the fifth year was 66.4%. Overall clinical performances were maintained very stable in NC facilities from this temporal data analysis. The hospitalization and survival rate also compare favorably with those reported internationally. These results warrant further studies to justify the application of this kind of quality assurance programs and performance management systems in dialysis care.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".