Chronic maintenance hemodialysis: Making sense of the terminology
Bibliographic record
Abstract
During the early decades, the hemodialysis (HD) terminology for modality, technique and function altered little as the widely accepted regime of thrice-weekly, 4-hourly dialysis varied little. In the last decade, however, a wide range of new options have emerged in all facets of HD therapy. This has led to a sudden expansion in terminology, some duplicating, some contradictory, some superfluous. The definitions used in 1 geographical region may mean something entirely different elsewhere, increasing cross-continental misunderstanding and misinterpretation and raising the often-asked question: "What exactly did the authors mean by that?" Although clearly the definitions used in this paper are also only the authors' opinion, we have sought to explore the use and sometimes confusing application of many commonly used terms, and we propose a number of possible deletions. Finally, we offer a descriptive data set that we believe should be used for all HD-related papers. Our conclusions will not always be welcomed--particularly by those who use terms we have rejected. Despite this, we believe it pertinent to fully review the dialysis terminology we use. Primarily, we hope to stimulate debate about which terms should be globally adopted and what those terms should mean when used. Although not all will agree with our conclusions, we hope this paper may provide a framework for a more streamlined, efficient, and globally acceptable nomenclature.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".