Optimal cerebrovascular reactivity thresholds for the determination of individualized intracranial pressure thresholds in traumatic brain injury: a CAHR-TBI cohort study
Bibliographic record
Abstract
Abstract It has been demonstrated that patient-specific intracranial pressure (ICP) thresholds are possible to derive using the function intersectionality between ICP and cerebrovascular reactivity (CVR). Such individualized ICP (iICP) thresholds represent a potential personalized medicine approach to neurocritical care management. However, it is currently unknown how various CVR thresholds compare in regard to deriving iICP. Here we attempt to identify the CVR thresholds that are best suited for iICP derivation. Leveraging 365 patient data sets from the CAnadian High-Resolution TBI (CAHR-TBI) Research Collaborative, iICP was derived using three ICP-based CVR indices: the pressure reactivity index (PRx); the pulse amplitude index (PAx); and the RAC index, and thresholds ranging from − 1 to + 1, in 0.05 increments. Patients were dichotomized based on 6-month outcome scores into Alive vs. Dead and Favorable vs. Unfavorable outcome. 2 × 2 tables were created for each threshold, grouping patients by outcome and whether their mean ICP was greater or less than their calculated iICP. Chi-squares were calculated for each table and subsequently plotted. The thresholds that produced the largest Chi-square values were identified as those able to derive the iICP with the greatest ability to predict outcomes. Next, Spearman rank correlation testing was used to evaluate associations between iICP, for each threshold, and measures of cerebral physiologic insult burden. With consideration of yield data, ability to predict outcome, and association with cerebral physiologic insult burden, a threshold of + 0.05 was identified for PRx. No optimal threshold could be identified for PAx or RAC.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".