Using dialysis machine technology to reduce intradialytic hypotension
Bibliographic record
Abstract
Intradialytic hypotension remains the most frequent complication associated with routine outpatient hemodialysis. Although increasing dialysis frequency and also lengthening dialysis session duration can reduce the risk of intradialytic hypotension, in practice, these options are limited to a small minority of dialysis patients. To help reduce intradialytic hypotension, a number of technological developments have been incorporated into the hemodialysis machine, based around relative blood volume monitoring, an indirect assessment of plasma volume. Further developments based on so called "fuzzy" logic feedback systems designed to adjust either or both the ultrafiltration rate and dialyzate sodium concentration according to relative changes in plasma volume. In addition, cooling and dissipation of the heat generated during dialysis also reduces the risk of intradialytic hypotension, and this can be regulated by cooling of the dialyzate using thermal control systems. In addition, convective therapies, such as online hemodialfiltration, have also been reported to reduce the frequency of intradialytic hypotension; whether this effect is simply due to increased cooling remains to be determined. Although all these developments have been reported to reduce the frequency of serious intradialytic hypotensive episodes, they have not been able to totally abolish hypotension, as they can not alone compensate for excessive weight gains and consequent excessive ultrafiltration requirements. Thus, in addition to the advances in hemodialysis machine technology designed to reduce intradialytic hypotension, attention also needs to be focused on reducing interdialytic weight gains, so reducing ultrafiltration requirement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".