Ventilatory, cerebrovascular, and cardiovascular interactions in acute hypoxia: regulation by carbon dioxide
Bibliographic record
Abstract
This study examined the effect of high, normal, and uncontrolled end-tidal Pco(2) (Pet(CO(2))) on the ventilatory, peak cerebral blood flow velocity (V(p)), and mean arterial blood pressure (MAP) responses to acute hypoxia. Nine healthy subjects undertook, in random order, three hypoxic protocols (end-tidal Po(2) was held at eight steps between 300 and 45 Torr) in conditions of hypercapnia, isocapnia, or poikilocapnia (Pet(CO(2)) +7.5 Torr, +1.0 Torr, or uncontrolled, respectively). Transcranial Doppler ultrasound was used to measure V(p) in the middle cerebral artery. The slopes of the linear regressions of ventilation, V(p), and MAP with arterial O(2) saturation were significantly greater in hypercapnia than in both isocapnia and poikilocapnia (P < 0.05). Strong, significant correlations were observed between ventilation, V(p), and MAP with each Pet(CO(2)) condition. These data suggest that 1). a high acute hypoxic ventilatory response (AHVR) decreases the acute hypoxic cerebral blood flow responses during poikilocapnia hypoxia, due to hypocapnic-induced cerebral vasoconstriction; and 2). in hypercapnic hypoxia, a high AHVR is associated with a high acute hypoxic cerebral blood flow response, demonstrating a linkage of individual sensitivities of ventilation and cerebral blood flow to the interaction of Pet(CO(2)) and hypoxia. In summary, the between-individual variability in AHVR is shown to be firmly linked to the variability in V(p) and MAP responses to hypoxia. Individuals with a high AHVR are found also to have high V(p) and MAP responses to hypoxia.
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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) | 0.000 | 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) | 0.001 | 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".