Bibliographic record
Abstract
Abstract Introduction Guidelines on hemodialysis (HD) dosing are based on urea kinetics, which can be modeled mathematically. Different schedules can be compared by equivalent continuous clearance. Methods A computer program generating HD prescriptions automatically was developed. In 200 actual HD urea kinetic modeling sessions involving 33 patients the urea distribution volume, generation rate, and concentration profile in the external space were computed using ionic dialysance as dialyzer clearance in the double pool ureakinetic model. Dialyzer in vivo mass area coefficient K0A was calculated from online data with the Michaels’ equation. Data from the modeling sessions were used for generating new prescriptions for the same patients. Findings New prescriptions meeting 12 criteria—technical limits and guideline targets—were generated by the model. They showed a wide range of basic treatment parameters (time and frequency, blood and dialysate flow, and ultrafiltration). In five cases, the dialysis time or frequency should have been increased from the actual to achieve the targets, in 72 cases the frequency could have been decreased. Two methods to emphasize RRF were tested. Using a coefficient of 2.0 for renal urea clearance in computing adjusted equivalent continuous clearance (EKR/Va), as suggested by Casino and Basile, decreased further the required treatment frequency in incremental dialysis. Discussion The model produced plausible individual prescriptions, but some unknown factors caused the determination of the dialyzer in vivo K0A to be inaccurate. The model must be tested with modern devices on patients before integration into dialysis machines and information systems.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".