Brain MRI CO2 Stress Testing: A Pilot Study in Patients with Concussion
Bibliographic record
Abstract
Background: There is a real need for quantifiable neuro-imaging biomarkers in concussion. Here we outline a brain BOLD-MRI CO2 stress test to assess the condition. Methods: This study was approved by the REB at the University of Manitoba. A group of volunteers without prior concussion were compared to post-concussion syndrome (PCS) patients – both symptomatic and recovered asymptomatic. Five 3-minute periods of BOLD imaging at 3.0 T were studied – baseline 1 (BL1 – at basal CO2 tension), hypocapnia (CO2 decreased,5 mmHg), BL2, hypercapnia (CO2 increased,10 mmHg) and BL3. Data were processed using statistical parametric mapping (SPM) for 1st level analysis to compare each subject’s response to the CO2 stress at the p = 0.001 level. A 2nd level analysis compared each PCS patient’s response to the mean response of the control subjects at the p = 0.05 level. Results: We report on 5 control subjects, 8 symptomatic and 4 asymptomatic PCS patients. Both increased and decreased response to CO2 was seen in all PCS patients in the 2 nd level analysis. The responses were quantified as reactive voxel counts: whole brain voxel counts (2.061.6%, p = 0.012 for symptomatic patients for CO2 response, controls and 3.065.1%, p = 0.139 for CO2 response. controls: 0.4960.31%, p = 0.053 for asymptomatic patients for CO2 response, controls and 4.466.8%, p = 0.281 for CO2 response. controls).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".