P.118 Near infrared spectroscopy based indices of cerebrovascular reactivity cluster with intracranial pressure based indices in moderate to severe TBI patients
Bibliographic record
Abstract
Background: Cerebrovascular reactivity has been identified as an important contributor to secondary injury following moderate to severe traumatic brain injury (TBI). “Gold-standard” intracranial pressure (ICP) based indies of cerebrovascular reactivity are limited by their invasive nature poor spatial resolution. Near infrared spectroscopy (NIRS) based indices of cerebrovascular reactivity are minimally invasive and have improved spatial resolution. In this study, classical machine-learning algorithms are leveraged to better characterize the relationship between these indices. Methods: High-resolution physiologic data was collected in a cohort of adult moderate to severe TBI patients. From this data both ICP and NIRS based indices of cerebrovascular reactivity were derived. Utilizing Agglomerative Hierarchical Clustering (AHC) and Principal Component Analysis, the relationship between these indices in higher dimensional physiologic space was examined. Results: A total of 83 patients with 314,395 minutes of unique and complete physiologic data was obtained. Through AHC and PCA there was higher order clustering between NIRS and ICP based indices, separate from other physiologic parameters. Conclusions: NIRS and ICP based indices of cerebrovascular reactivity relate to one another in higher dimensional physiologic space. NIRS based indices of cerebrovascular reactivity may be a viable alternative to ICP based indices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".