Bibliographic record
Abstract
Abstract Survival of patients on hemodialysis remains poor, but the benefits of increasing urea clearance have probably been maximized within our current treatment schedules. Long dialysis sessions (8 hr) produce impressive outcomes, with mortality 53% to 55% lower than conventional schedules. Even increasing from 4 to 5 hr may improve survival. Increased frequency of dialysis (6 times weekly) produces impressive reductions in left ventricular mass and could conceivably be implemented in‐center. Preliminary data suggest a 61% reduction in mortality with increased frequency. Nightly dialysis combines longer sessions with increased frequency and has produced remarkable clinical gains in blood pressure, left ventricular mass, serum phosphate, and sleep apnea. However, the data are mainly from case series and impact on mortality remains unknown. Expansion of home hemodialysis would be necessary for this modality to grow. Convective therapies remove middle molecules more effectively, and observational data suggest hemodiafiltration has the potential to improve mortality by 35% to 36%. Hemodiafiltration has the advantage of being relatively easy to implement. The uremic milieu is complex and further investigation of the underlying pathophysiology is needed to inform future dialysis interventions. The survival data above are from observational studies, and hence benefits are likely to be exaggerated. Randomized trials of dialysis interventions are desperately needed. They remain difficult to perform, because of the complexity of both the patient population and the interventions, and because of limited available funding.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".