Longer and better lives for patients … and their centers: A strategy for building a home hemodialysis program
Bibliographic record
Abstract
Physicians should prescribe the dialysis mode most likely to result in the best outcome for the end-stage renal disease patient, not leave it to the patient or dialysis center to choose. That prescription, in order of decreasing desirability, should be for frequent home nocturnal hemodialysis, frequent home short-daily, or least efficacious, 3x in-center or peritoneal dialysis. Patient limitations may require prescribing a less than optimal mode. Physician-patient discussions should focus on expected clinical outcomes and health benefits, not patient convenience or "lifestyle." In order to overcome natural fears, qualified patients should participate in a short in-center frequent dialysis personal clinical trial to experience the benefits. The financial health of dialysis centers will be enhanced by shifting continually inflating labor costs from the center to patients and home caregivers. This shift from 3x in-center to frequent (optimally 6x nocturnal) home dialysis may reasonably be expected to enhance the survival and well-being of both the patient and the center.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".