Continuous online monitoring of ionic dialysance allows modification of delivered hemodialysis treatment time
Bibliographic record
Abstract
Considerable intrinsic intrapatient variability influences the actual delivery of Kt/V. The aim of this study is to examine the feasibility of using continuous online assessment of ionic dialysance measurements (Kt/V(ID)) to allow dialysis sessions to be altered on an individual basis. Ten well-established chronic hemodialysis (HD) patients without significant residual renal function were studied (mean age 65+/-4.3 [38-81] years, mean length of time on dialysis 66+/-18 [14-189] months). These patients had all been receiving thrice-weekly 4-hr dialysis using Integra dialysis monitors. Dialysis monitors were equipped with Diascan modules permitting measurement of Kt/V(ID). Predicted treatment time required to achieve a Kt/V(ID) > or = 1.1 (equivalent to a urea-based method of 1.2) was calculated from the delivered Kt/V(ID) at 60 and 120 min. Treatment time was reprogrammed at 2 hr (ensuring all planned ultrafiltration would be accommodated into the new modified session duration). Owing to practical issues, and to avoid excessively short dialysis times, these changes were censored at no more than+/-10% of the usual 240-min treatment time (210-265 min). Data were collected from a total of 50 dialysis sessions. Almost all sessions (47/50) required modification of the standard treatment time: 13/50 sessions were lengthened and 34/50 shortened (mean length of session 232.2+/-2.5 [210-265] min). A Kt/V(ID) of > or = 1.1 was achieved in 39/50 sessions. The difference in mean urea-based Kt/V poststudy (1.3+/-0.05 [1.1-1.6]) and mean achieved Kt/V(ID) (1.16+/-0.02 [0.7-1.37]) was significant (p = 0.002). The use of individualized variable dialysis treatment time using online ionic dialysance measurements of Kt/V(ID) appears both practicable and effective at ensuring consistently delivered adequate dialysis.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".