Severe Traumatic Brain Injury in French-Speaking Pediatric Intensive Care Units: Study of Practices
Bibliographic record
Abstract
Abstract Best strategies for managing severe pediatric traumatic brain injury (TBI) are not established, with wide variations among professional practices. The main objective of this study was to assess compliance with updated pediatric TBI management guidelines (2019). A survey was distributed among French-speaking pediatric intensive care physicians from April 1 to June 30, 2019. The survey was based on a clinical case with a total of 70 questions that cover the 15 items of the 2019 TBI guidelines. The questions evaluated the assessment and management of TBI during the acute and intensive care phases. Of 487 e-mails sent, 78 surveys were included. Guidelines were adhered to (> 60%) for 10 of 15 items in the guidelines. Strong adherence to recent guideline changes was achieved for seizure prophylaxis with levetiracetam (n = 21/33, 64%) and partial pressure of carbon dioxide threshold (n = 52, 67%). However, management of the sodium and glucose thresholds and the role of transcranial Doppler were not consistent with the guidelines. Assessment of brain tissue oxygenation (n = 12, 16%) and autoregulation (n = 35, 45%) was not a common practice. There was strong agreement among clinicians on the intracranial pressure (> 80%) and cerebral perfusion pressure (> 70%) thresholds used according to age. Overall, stated practices for the management of TBI appear to be relatively standardized among responders. Variations persist in areas with a lack of evidence and pediatric-specific recommendations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".