Pregnancy on short‐daily home hemodialysis using low dialysate flow rate: A new hope for the end‐stage renal disease patients
Bibliographic record
Abstract
INTRODUCTION: In France in 2014, there were approximately 1500 patients of reproductive age treated by dialysis. Pregnancy in these patients remains rare, however, the incidence has increased since the 2000s, with a parallel increase in the fetal survival rate. We report 2 cases of pregnancy in short-daily home hemodialysis using low dialysate flow rate. METHODS: Short-daily hemodialysis was continued at the request of the patients. The treatment consisted in an increase of frequency and duration of hemodialysis sessions, an independent blood pressure and dry weight control supervised by nephrological monitoring twice a month and a regular obstetrics follow-up. FINDINGS: Both patients continued hemodialysis at home until delivery and gave birth to 2 moderately premature babies, without other complication and resumed short-daily home hemodialysis fastly after delivery. CONCLUSION: Short-daily hemodialysis using low dialysate flow rate during pregnancy seems to allow a good control of uremia and blood pressure without requiring a major increase of weekly dialysis duration. Therefore, it could become an alternative to other hemodialysis programs while allowing the patients to continue their treatment at home. However, other studies are necessary in order to define the position of this procedure during pregnancy.
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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 | 0.001 |
| 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 | 0.001 |
| 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".