Iatrogenic hypernatremia in hemodialysis patients: A result of erroneous online conductivity monitor and conductivity meter reading
Bibliographic record
Abstract
Hyponatremia is common in chronic kidney disease and in end stage kidney disease (ESKD) but hypernatremia is infrequent in ESKD. The incidence of hypernatremia is higher in ambulatory peritoneal dialysis (PD) than in hemodialysis (HD) patients. In PD patients it is often a result of excessive ultrafiltration but in HD it is often a result of dialysate composition errors. Dialysate composition errors can inadvertently cause either hyponatremia or hypernatremia. We present two cases of symptomatic hypernatremia which manifested as increased thirst, excessive weight gain and worsening hypertension in HD patients. The hypernatremia was caused by a combination of errors in online conductivity reading and a faulty hand held conductivity meter. Symptoms were relieved in both patients after replacement of the dialysis machine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".