Protocol-dependence of middle cerebral artery dilation to modest hypercapnia
Bibliographic record
Abstract
There is a need for improved understanding of how different cerebrovascular reactivity (CVR) protocols affect vascular cross-sectional area (CSA) to reduce error in CVR calculations when measures of vascular CSA are not feasible. In human participants, we delivered ∼±4 mm Hg end-tidal partial pressure of CO 2 (P ET CO 2 ) relative to baseline through controlled delivery, and measured changes in middle cerebral artery (MCA) CSA (7 Tesla magnetic resonance imaging (MRI)), blood velocity (transcranial Doppler and Phase contrast MRI), and calculated CVR based on a 3-minute steady-state (+4 mm Hg P ET CO 2 ) and a ramp (−3 to +4 mm Hg of P ET CO 2 ). We observed that (1) the MCA did not dilate during the ramp protocol (slope for CSA across time P > 0.05; R 2 = 0.006), but did dilate by ∼7% during steady-state hypercapnia (P < 0.05); and (2) MCA blood velocity CVR was not different between ramp and steady-state hypercapnia protocols (ramp: 3.8 ± 1.7 vs. steady-state: 4.0 ± 1.6 cm/s/mm Hg), although calculated MCA blood flow CVR was ∼40% greater during steady-state hypercapnia than during ramp (P < 0.05) with the discrepancy due to MCA CSA changes during steady-state hypercapnia. We propose that a ramp model, across a delta of −3 to +4 mm Hg P ET CO 2 , may provide an alternative approach to collecting CVR measures in young adults with transcranial Doppler when CSA measures are not feasible. Novelty: We optimized a magnetic resonance imaging sequence to measure dynamic middle cerebral artery (MCA) cross-sectional area (CSA). A ramp model of hypercapnia elicited similar MCA blood velocity reactivity as the steady-state model while maintaining MCA CSA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".