Mannitol dosing error during interfacility transfer for intracranial emergencies
Bibliographic record
Abstract
OBJECT: Mannitol is commonly used to treat elevated intracranial pressure (ICP). The authors analyzed mannitol dosing errors at peripheral hospitals prior to or during transport to tertiary care facilities for intracranial emergencies. They also investigated the appropriateness of mannitol use based on the 2007 Brain Trauma Foundation guidelines for severe traumatic brain injury. METHODS: The authors conducted a retrospective review of the Shock Trauma Air Rescue Society (STARS) electronic patient database of helicopter medical evacuations in Alberta, Canada, between 2004 and 2012, limited to patients receiving mannitol before transfer. They extracted data on mannitol administration and patient characteristics, including diagnosis, mechanism, Glasgow Coma Scale score, weight, age, and pupil status. RESULTS: A total of 120 patients with an intracranial emergency received a mannitol infusion initiated at a peripheral hospital (median Glasgow Coma Scale score 6; range 3-13). Overall, there was a 22% dosing error rate, which comprised an underdosing rate (<0.25 g/kg) of 8.3% (10 of 120 patients), an overdosing rate (>1.5 g/kg) of 7.5% (9 of 120), and a nonbolus administration rate (>1 hour) of 6.7% (8 of 120). Overall, 72% of patients had a clear indication to receive mannitol as defined by meeting at least one of the following criteria based on Brain Trauma Foundation guidelines: neurological deterioration (11%), severe traumatic brain injury (69%), or pupillary abnormality (25%). CONCLUSIONS: Mannitol administration at peripheral hospitals is prone to dosing error. Strategies such as a pretransport checklist may mitigate this risk.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".