A Promising Subject-Level Classification Model for Acute Concussion Based on Cerebrovascular Reactivity Metrics
Bibliographic record
Abstract
Concussion imaging research has primarily focused on neuronal disruption with lesser emphasis directed toward vascular dysfunction. However, blood flow metrics may be more sensitive than measures of neuronal integrity. Vascular dysfunction can be assessed by measuring cerebrovascular reactivity (CVR)—the change in cerebral blood flow per unit change in vasodilatory stimulus. CVR metrics, including speed and magnitude of flow responses to a standardized well-controlled vasoactive stimulus, are potentially useful for assessing individual subjects following concussion given that blood flow dysregulation is known to occur with traumatic brain injury. We assessed changes in CVR metrics to a standardized vasodilatory stimulus during the acute phase of concussion. Using a case control design, 20 concussed participants and 20 healthy controls (HCs) underwent CVR assessment measuring blood oxygen–level dependent (BOLD) magnetic resonance imaging using precise changes in end-tidal partial pressure of CO 2 (PETCO 2 ). Metrics were calculated for the whole brain, gray matter (GM), and white matter (WM) using sex-stratification. A leave-one-out receiver operating characteristic (ROC) analysis classified concussed from HCs based on CVR metrics. CVR magnitude was greater and speed of response faster in concussed participants relative to HCs, with WM showing higher classification accuracy compared with GM. ROC analysis for WM-CVR metrics revealed an area under the curve of 0.94 in males and 0.90 in females for speed and magnitude of response respectively. These greater than normal responses to a vasodilatory stimulus warrant further investigation to compare the predictive ability of CVR metrics against structural injury metrics for diagnosis and prognosis in acute concussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".