Endotoxemia after high cutoff hemodialysis for treatment of patient with multiple myeloma can be prevented by using ultrapure dialysate: A case report
Bibliographic record
Abstract
To report endotoxemia presented in a case with multiple myeloma (MM) treated by high cutoff hemodialysis (HCO-HD) being prevented by using ultrapure dialysate. A female inpatient with MM received six times HCO-HD (HCO 2100 dialyzer) within 3 weeks after initiation of a chemotherapy based on vincristine+epirubicin+dexamethasone protocol. Conventional dialysate was used in the first three times and then changed to ultrapure dialysate due to elevation of body temperature after HCO-HD. Free light chains (FLC) and endotoxin levels in blood and dialysate were monitored. After six times HCO-HD, her serum FLC λ decreased from 4689 mg/L to 492.7 mg/L, with a trend of decline of serum creatinine. The clearance, reduction ratio, and removal amount of FLC λ was 38.4 mL/min, 71.0-85.2%, and 9.06-18.02 g, respectively, in the setting of dialysate flow rate 500 mL/min, while in the setting of dialysate flow rate 200 mL/min, the removal efficacy of FLC λ was lower than the former. A rise of body temperature up to 38.5°C after treatment and endotoxemia (endotoxin levels 0.122 EU/mL) was found when using conventional dialysate (endotoxin levels 0.112-0.145 EU/mL), but not seen after changing to ultrapure dialysate. Combined with appropriate chemotherapy, HCO-HD can effectively remove and reduce blood FLC. Attention should be paid to the endotoxemia and the rise of temperature after treatment when conventional dialysate is used, which can be prevented by using ultrapure dialysate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".