At What Point Is the Accumulated Evidence Sufficient to Change Maintenance Intravenous Fluid Prescribing Practice in Children? How About Yesterday?
Bibliographic record
Abstract
It is intriguing and somewhat disturbing that despite intravenous fluids (IVFs) being the most commonly prescribed therapy in hospitalized children, there still seems to be such a wide variation in the approach to choosing what type of intravenous (IV) maintenance fluid to use1,2 and the required monitoring that goes along with it. In this issue of Hospital Pediatrics , Rooholamini et al3 have attempted to address the discrepancy between evidence and practice through a quality improvement project using a standardized pathway to guide the choice of maintenance IVF. Their multidisciplinary approach included creating an evidence-based clinical pathway synthesizing the current consensus with regards to indications of when to start and discontinue IVFs and especially with regards to the optimal tonicity of the IVFs prescribed. They also included standardized monitoring for dysnatremia and signs of fluid overload. I would recommend readers electronically access the clinical pathway described by Romero et al4 because it is well designed with many good principles prominently highlighted (eg, “do not use 1/4 NS for maintenance fluids,” and “pay attention to weight fluctuations ±3%”) and presents the latest evidence in a pragmatic, algorithmic fashion. Those with increased antidiuretic hormone (ADH) secretion risk factors are recommended to receive 0.9% saline, whereas the rest are given 0.45% saline. Some guidance on total IVF maintenance calculation is also given. In the section on monitoring, they have recommended that all children receiving >75% of their maintenance by IVF have their sodium (Na) checked within 24 hours of initiation, but only those on hypotonic fluids require routine daily checks if getting >75% maintenance IVF. Their approach has tried to accommodate perceived gaps and areas of continuing controversy in the evidence (eg, potential adverse effects of isotonic fluids and frequency of Na monitoring). The most relevant and …
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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 | 0.041 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".