Ventilation monitoring for severe pediatric traumatic brain injury during interfacility transport
Bibliographic record
Abstract
BACKGROUND: Ventilation monitoring practice for intubated pediatric patients with severe traumatic brain injury (TBI) during interfacility transport (IFT) has not been well documented. We describe the difference of practices in ventilation monitoring during IFT from the perspective of a level I pediatric trauma center with an enormous catchment area. METHODS: Patients admitted between July 2008 and September 2013 at Winnipeg Health Science Center, Canada, were examined in this retrospective chart review. All patients with severe TBI were intubated in regional health centers and required transport to the level 1 trauma center. Injuries due to inflicted head trauma (<5 years of age), stroke, drowning, and asphyxia were excluded. Patient characteristics, injury data, ventilation monitoring, and transport metrics were obtained from a regional health center, and transport and trauma center charts. RESULTS: Thirty four patients were studied. Specialty transport teams utilized ventilation monitoring significantly more often (95 vs. 23 %; p < 0.001) than non-specialized ground transport. Specialty teams were more likely to obtain a blood gas prior to departure (74 vs. 0 %; p = 0.037) if end-tidal monitoring was used. Among unmonitored ground transport patients, mean transport time was 69.1 min. CONCLUSIONS: Non-specialized ground IFT teams did not reliably monitor ventilation in intubated severe pediatric TBI patients. Blood gas monitoring was not a ubiquitous practice for either team. Optimal ventilation monitoring strategies for severe pediatric TBI may require both blood gas and end-tidal monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".