Avoiding hypotonic solutions in paediatrics: Keeping our patients safe
Bibliographic record
Abstract
A previously healthy, fully immunized, 14 kg four-year-old girl presented to the local emergency department with a seven-day history of upper respiratory tract symptoms and 24 h of fever, irritability and decreased oral intake. Her temperature on presentation was 38.9°C, heart rate 120 beats/min, respiratory rate 25 breaths/min, and blood pressure 90/60 mmHg. She was alert but irritable on physical examination, mildly volume-depleted and had evidence of meningismus. Her electrolytes were normal, notably a serum sodium concentration of 138 mmol/L, and there was pleocytosis of the cerebrospinal fluid. The patient was admitted with a working diagnosis of bacterial meningitis, given a 20 mL/kg normal saline bolus and commenced on intravenous (IV) vancomycin and ceftriaxone. She was started on maintenance IV fluids, consisting of dextrose 3.3% in NaCl 0.3% (‘2/3 – 1/3’) with KCl 20 mmol/L at 50 mL/h. She was also permitted to drink oral fluids as tolerated. Twenty-four hours later, the child's irritability improved; however, she was noted to have headache and nausea. She was assessed by the paediatrician who reassured the family that these symptoms were consistent with meningitis. Her urine output was noted to be adequate, though it was not specifically quantified. Electrolytes were not repeated.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".