Impact of kidney transplantation on sleep apnoea in patients with end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: Sleep apnoea is common in patients with end-stage renal disease. Although individual case reports have described an improvement in sleep apnoea following kidney transplantation, there have been no longitudinal studies of a case series to determine what proportion of patients with sleep apnoea improve. METHODS: Dialysis-dependent patients awaiting kidney transplantation and pre-dialysis patients with an identified living donor kidney had overnight polysomnography, which was repeated several months after successful kidney transplantation. Patients were divided into apnoeic and non-apnoeic groups based on an apneoa-hypopnoea index (AHI) > 10/h during pre-transplant polysomnography and, following transplantation, apnoeic patients were further divided into responders and non-responders based on >50% reduction in AHI and/or AHI < 10/h. RESULTS: Eighteen patients (11 men, 7 women), aged 27-65, were studied. Pre-transplant sleep apnoea was present in 11 of 18 (61%) patients. Although transplantation was associated with a significant reduction in blood urea nitrogen and serum creatinine, there were no significant changes in AHI (pre vs post: 20.2 +/- 15.1 vs 23.5 +/- 21.3). Among the 11 apnoeic patients, only three met the criteria for a significant improvement ('responder'). There were no patient characteristics, sleep apnoea indices or renal function changes that distinguished responders from non-responders. CONCLUSIONS: Sleep apnoea improves in a minority of patients with end-stage renal disease following successful kidney transplantation. Specific determinants of improvement were not identified.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".