Bibliographic record
Abstract
Care of patients with end-stage renal disease (ESRD) is essential but also resource intense. We review several studies on online hemodiafiltration (OL-HDF), which concluded that high-volume OL-HDF is associated with better outcome compared to conventional hemodialysis. The cost-effectiveness of OL-HDF was shown in many studies. For example, in the Canadian setting of the Convective Transport Study (CONTRAST), the high-efficiency OL-HDF was shown to be cost-effective compared with low-flux hemodialysis (LF-HD) for patients with ESRD. In our study (Al Saran et al.), it was shown that the cost of hemodialysis was quite less in Saudi Arabia than in other industrialized countries while maintaining a high standard of care. In our retrospective analysis of the cost of OL-HDF in the same center, it was only 3% higher than the conventional HD, which indicates that it is cost-effective considering the improved hospitalization rate, the mortality rates, and the likely better quality of life associated with it. The trend of increased practice of OL-HDF may encourage the practice of home OL-HDF as well. It has been shown that home HD is more cost-effective than in-center HD and we presume that the same results will be applied to home OL-HDF as well.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".