Treatment Policy rather than Patient Characteristics Determines Convection Volume in Online Post-Dilution Hemodiafiltration
Bibliographic record
Abstract
BACKGROUND/AIMS: Sub-analyses of three large trials showed that hemodiafiltration (HDF) patients who achieved the highest convection volumes had the lowest mortality risk. The aims of this study were (1) to identify determinants of convection volume and (2) to assess whether differences exist between patients achieving high and low volumes. METHODS: HDF patients from the CONvective TRAnsport STudy (CONTRAST) with a complete dataset at 6 months (314 out of a total of 358) were included in this post hoc analysis. Determinants of convection volume were identified by regression analysis. RESULTS: Treatment time, blood flow rate, dialysis vintage, serum albumin and hematocrit were independently related. Neither vascular access nor dialyzer characteristics showed any relation with convection volume. Except for some variation in body size, patient characteristics did not differ across tertiles of convection volume. CONCLUSION: Treatment time and blood flow rate are major determinants of convection volume. Hence, its magnitude depends on center policy rather than individualized patient prescription.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".