Pediatric Bone Marrow Transplant (BMT) Recipients with Acute Renal Failure (ARF): Assessment of an Algorithm to Prevent Fluid Overload including Early Initiation of Renal Replacement Therapy (RRT)
Bibliographic record
Abstract
Objective: ARF with fluid overload (FO) occurs often in BMT recipients. We have demonstrated increasing %FO prior to CRRT initiation is associated with mortality in children with ARF. Based on these data, we devised a protocol for FO prevention in BMT pts with ARF. BMT pts with ARF and 5% FO were started on furosemide and low-dose dopamine. To allow for nutrition, medication and blood product administration, RRT was initiated for pts with ≥ 10% FO. We reviewed the course and outcome for pediatric BMT pts with ARF and fluid overload managed with this protocol. Subjects: Medical records of 29 BMT pts with 33 ARF episodes from Jan 99 to Jan 02 were reviewed. Mean pt age was 12.8 ± 5 yrs (2–23.5 yrs). Outcome: 14/29 (48%) pts survived an initial ARF episode. 0/4 pts survived a second ARF episode. 14/14 survivors (S) either maintained ≤10% FO during course or re-attained ≤10%FO with RRT treatment. Max %FO for S was 17%. 7/19 non-survivors (NS) were <10% FO at the time of death. 4/18 (22%) pts who received RRT (3 HD, 15 CRRT) survived. 2/15 (13%) CRRT pts survived. Mechanical ventilation, Pediatric Risk of Mortality score ≥10 at ICU admission and use of >1 pressors were associated with lower survival (p < 0.05). Neither GVHD nor septic shock correlated with survival. Conclusion: Our data demonstrate that maintenance of euvolemia (%FO ≤10% is critical for S in BMT patients with ARF as all non-euvolemic pts died. We suggest that aggressive management with diuretics and earlier RRT initiation in pts not responsive to diuretics may improve BMT pt survival.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".