Two-breath CO<sub>2</sub> test detects altered dynamic cerebrovascular autoregulation and CO<sub>2</sub> responsiveness with changes in arterial P<scp>co</scp><sub>2</sub>
Bibliographic record
Abstract
The new two-breath CO(2) method was employed to test the hypotheses that small alterations in arterial P(CO(2)) had an impact on the magnitude and dynamic response time of the CO(2) effect on cerebrovascular resistance (CVRi) and the dynamic autoregulatory response to fluctuations in arterial pressure. During a 10-min protocol, eight subjects inspired two breaths from a bag with elevated P(CO(2)), four different times, while end-tidal P(CO(2)) was maintained at three levels: hypocapnia (LoCO(2), 8 mmHg below resting values), normocapnia, and hypercapnia (HiCO(2), 8 mmHg above resting values). Continuous measurements were made of mean blood pressure corrected to the level of the middle cerebral artery (BP(MCA)), P(CO(2)) (estimated from expired CO(2)), and mean flow velocity (MFV, of the middle cerebral artery by Doppler ultrasound), with CVRi = BP(MCA)/MFV. Data were processed by a system identification technique (autoregressive moving average analysis) with gain and dynamic response time of adaptation estimated from the theoretical step responses. Consistent with our hypotheses, the magnitude of the P(CO(2))-CVRi response was reduced from LoCO(2) to HiCO(2) [from -0.04 (SD 0.02) to -0.01 (SD 0.01) (mmHg x cm(-1) x s) x mmHg Pco(2)(-1)] and the time to reach 95% of the step plateau increased from 12.0 +/- 4.9 to 20.5 +/- 10.6 s. Dynamic autoregulation was impaired with elevated P(CO(2)), as indicated by a reduction in gain from LoCO(2) to HiCO(2) [from 0.021 +/- 0.012 to 0.007 +/- 0.004 (mmHg x cm(-1) x s) x mmHg BP(MCA)(-1)], and time to reach 95% increased from 3.7 +/- 2.8 to 20.0 +/- 9.6 s. The two-breath technique detected dependence of the cerebrovascular CO(2) response on P(CO(2)) and changes in dynamic autoregulation with only small deviations in estimated arterial P(CO(2)).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".