Measuring residual renal function for hemodialysis adequacy: Is there an easier option?
Bibliographic record
Abstract
Most patients starting hemodialysis (HD) have residual renal function. As such, there has been increased interest in starting patients with less frequent and shorter dialysis session times. However, for this incremental approach to be successful, patients require regular monitoring of residual renal function, so that as residual renal function declines, the amount of HD is appropriately increased. Currently most dialysis centers rely on interdialytic urine collections. However, many patients find these inconvenient and there may be marked intrapatient variability due to compliance issues. Thus, alternative markers of residual renal function are required for routine clinical practice. Currently three middle sized molecules; cystatin C, β2 microglobulin, and βtrace protein have been investigated as potential endogenous markers of glomerular filtration. Although none is ideal, combinations of these markers have been proposed to provide a more accurate estimation of glomerular clearance, and in particular cut offs for minimal residual renal function. However, in patients with low levels of residual renal function it remains unclear as to whether the benefits of residual renal function equally apply to glomerular filtration or tubular function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".