P.119 Quantification of the relationship between near infrared spectroscopy based and intracranial pressure based indices of cerebrovascular reactivity in moderate to severe TBI
Bibliographic record
Abstract
Background: Cerebrovascular reactivity is an important contributor to secondary injury following traumatic brain injury (TBI). The poor spatial resolution and invasive nature of “Gold-standard” intracranial pressure (ICP) based indies of cerebrovascular reactivity limit their use. Near infrared spectroscopy (NIRS) based indices of cerebrovascular reactivity are minimally invasive and have improved spatial resolution. The precise relationship between NIRS and ICP based indices is quantified utilizing times series analysis and advanced statistical techniques. Methods: High-resolution physiologic data was collected in a cohort of adult moderate to severe TBI patients at a single quaternary care site. From this data both ICP and NIRS based indices of cerebrovascular reactivity were derived. The times series structure of these indices was determined and used to correct for autocorrelation in a linear mixed effects model of ICP based indices from NIRS based indices of cerebrovascular reactivity. Results: A total of 83 patients were included in this study. Times series analysis coupled with mixed effects modeling was utilized to examine the relationship between ICP and NIRS based indices of cerebrovascular reactivity. Conclusions: Times series analysis coupled with mixed effects modeling allows for a more complete understanding of the relationship between ICP and NIRS based indices of cerebrovascular reactivity in the setting TBI.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".