Longitudinal study of cerebral blood flow regulation during exercise in pregnancy
Bibliographic record
Abstract
Cerebrovascular adaptation to pregnancy is poorly understood. We sought to assess cerebrovascular regulation in response to visual stimulation, hypercapnia and exercise across the three trimesters of pregnancy. Using transcranial Doppler (TCD) ultrasound, middle and posterior cerebral artery mean blood velocities (MCAv mean and PCAv mean ) were measured continuously at rest and in response to (1) visual stimulation to assess neurovascular coupling (NVC); (2) a modified Duffin hyperoxic CO 2 rebreathe test, and (3) an incremental cycling exercise test to volitional fatigue in non-pregnant ( n = 26; NP) and pregnant women (first trimester [ n = 13; TM1], second trimester [ n = 21; TM2], and third trimester [ n = 20; TM3]) in total 47 women. At rest, MCAv mean and P ET CO 2 were lower in TM2 compared to NP. PCAv mean was lower in TM2 but not TM1 or TM3 compared to NP. Cerebrovascular reactivity in MCAv mean and PCAv mean during the hypercapnic rebreathing test was not different between pregnant and non-pregnant women. MCAv mean continued to increase over the second half of the exercise test in TM2 and TM3, while it decreased in NP due to differences in ΔP ET CO 2 between groups. Pregnant women experienced a delayed decrease in MCAv mean in response to maximal exercise compared to non-pregnant controls which was explained by CO 2 reactivity and P ET CO 2 level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".