Perceived Utility of Intracranial Pressure Monitoring in Traumatic Brain Injury: A Seattle International Brain Injury Consensus Conference Consensus-Based Analysis and Recommendations
Bibliographic record
Abstract
BACKGROUND: Intracranial pressure (ICP) monitoring is widely practiced, but the indications are incompletely developed, and guidelines are poorly followed. OBJECTIVE: To study the monitoring practices of an established expert panel (the clinical working group from the Seattle International Brain Injury Consensus Conference effort) to examine the match between monitoring guidelines and their clinical decision-making and offer guidance for clinicians considering monitor insertion. METHODS: We polled the 42 Seattle International Brain Injury Consensus Conference panel members' ICP monitoring decisions for virtual patients, using matrices of presenting signs (Glasgow Coma Scale [GCS] total or GCS motor, pupillary examination, and computed tomography diagnosis). Monitor insertion decisions were yes, no, or unsure (traffic light approach). We analyzed their responses for weighting of the presenting signs in decision-making using univariate regression. RESULTS: Heatmaps constructed from the choices of 41 panel members revealed wider ICP monitor use than predicted by guidelines. Clinical examination (GCS) was by far the most important characteristic and differed from guidelines in being nonlinear. The modified Marshall computed tomography classification was second and pupils third. We constructed a heatmap and listed the main clinical determinants representing 80% ICP monitor insertion consensus for our recommendations. CONCLUSION: Candidacy for ICP monitoring exceeds published indicators for monitor insertion, suggesting the clinical perception that the value of ICP data is greater than simply detecting and monitoring severe intracranial hypertension. Monitor insertion heatmaps are offered as potential guidance for ICP monitor insertion and to stimulate research into what actually drives monitor insertion in unconstrained, real-world conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".