Bibliographic record
Abstract
Abstract Hemodialysis was a neglected aspect of nephrology in the UK. At the request of the Renal Association, the first UK Haemodialysis Masterclass was organized in 2007. The articles in this supplement arose from that meeting. Here, an overview of UK hemodialysis services and nephrology training is presented as background. Government‐funded dialysis should be provided to all UK citizens who require it. In 2005, there were 17,645 patients receiving hemodialysis, 5057 on peritoneal dialysis and 19,074 with kidney transplants, looked after by 359 nephrologists working in 73 National Health Service renal units. Renal replacement therapy incidence and prevalence remain comparatively low, at 108 and 694 per million population, respectively. Whether this represents inadequate provision or genuinely lower need remains unclear. The Renal Association sets clinical practice guidelines for dialysis, and audits performance via the UK Renal Registry. Postgraduate medical education is undergoing radical change in the UK. This is driven by the reduction in trainee doctors' working hours to 48 hr/week (mandated by the European Working Time Directive), and the governments' wish to reduce the duration of training, but also by a desire to formalize training, Our challenge is to continue to produce talented clinical nephrologists educated in breadth and depth, despite the reduced emphasis on clinical experience and omission of period of scientific research. The future for hemodialysis services in the UK is, however, promising with an expansion in the number of specialists and dialysis centers, and a growing interest in dialysis practice and research.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".