Measuring the human ventilatory and cerebral blood flow response to CO<sub>2</sub>: a technical consideration for the end-tidal-to-arterial gas gradient
Bibliographic record
Abstract
Our aim was to quantify the end-tidal-to-arterial gas gradients for O2 (PET-PaO2) and CO2 (Pa-PETCO2) during a CO2 reactivity test to determine their influence on the cerebrovascular (CVR) and ventilatory (HCVR) response in subjects with (PFO+, n = 8) and without (PFO-, n = 7) a patent foramen ovale (PFO). We hypothesized that 1) the Pa-PETCO2 would be greater in hypoxia compared with normoxia, 2) the Pa-PETCO2 would be similar, whereas the PET-PaO2 gradient would be greater in those with a PFO, 3) the HCVR and CVR would be underestimated when plotted against PETCO2 compared with PaCO2, and 4) previously derived prediction algorithms will accurately target PaCO2. PETCO2 was controlled by dynamic end-tidal forcing in steady-state steps of -8, -4, 0, +4, and +8 mmHg from baseline in normoxia and hypoxia. Minute ventilation (V̇E), internal carotid artery blood flow (Q̇ICA), middle cerebral artery blood velocity (MCAv), and temperature corrected end-tidal and arterial blood gases were measured throughout experimentation. HCVR and CVR were calculated using linear regression analysis by indexing V̇E and relative changes in Q̇ICA, and MCAv against PETCO2, predicted PaCO2, and measured PaCO2. The Pa-PETCO2 was similar between hypoxia and normoxia and PFO+ and PFO-. The PET-PaO2 was greater in PFO+ by 2.1 mmHg during normoxia (P = 0.003). HCVR and CVR plotted against PETCO2 underestimated HCVR and CVR indexed against PaCO2 in normoxia and hypoxia. Our PaCO2 prediction equation modestly improved estimates of HCVR and CVR. In summary, care must be taken when indexing reactivity measures to PETCO2 compared with PaCO2.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".