Bibliographic record
Abstract
Incremental peritoneal dialysis (PD) has been variably defined. It involves taking advantage of the residual renal function that is usually present at initiation of dialysis to initially prescribe less onerous lower doses of PD while still achieving individualized clearance goals. We propose that incremental PD be defined as a strategy, rather than a particular regime, in which: (1) less than standard "full-dose" PD is initially prescribed in recognition of the value of residual renal function; (2) peritoneal clearance is initially less than the individualized clearance goal but the combination of peritoneal plus renal clearance achieves or exceeds that goal clearance; and (3) there is a clear intention to increase dose of PD as renal clearance declines and/or symptoms appear. Incremental PD by its nature lessens the workload of dialysis for those doing PD, reduces cost and exposure of the peritoneal membrane to glucose, and may lessen mechanical symptoms. Evidence that incremental PD improves clinical outcomes compared to the use of full-dose PD is lacking but one randomized controlled trial, multiple observational studies, and a systematic review all suggest that outcomes are at least as good. Given that incremental PD costs less and is inherently less onerous, it is reasonable, pending larger randomized trials, to adopt this strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".