Influence of hypercapnia and hypercapnic hypoxia on the heart rate response to apnea
Bibliographic record
Abstract
Abstract We aimed to determine the relative contribution of hypercapnia and hypoxia to the bradycardic response to apneas. We hypothesized that apneas with hypercapnia would cause greater bradycardia than normoxia, similar to the response seen with hypoxia, and that apneas with hypercapnic hypoxia would induce greater bradycardia than hypoxia or hypercapnia alone. Twenty‐six healthy participants (12 females; 23 ± 2 years; BMI 24 ± 3 kg/m2) underwent three gas challenges: hypercapnia (+5 torr end tidal partial pressure of CO2 [PETCO2]), hypoxia (50 torr end tidal partial pressure of O2 [PETO2]), and hypercapnic hypoxia (combined hypercapnia and hypoxia), with each condition interspersed with normocapnic normoxia. Heart rate and rhythm, blood pressure, PETCO2, PETO2, and oxygen saturation were measured continuously. Hypercapnic hypoxic apneas induced larger bradycardia (−19 ± 16 bpm) than normocapnic normoxic apneas (−11 ± 15 bpm; p = 0.002), but had a comparable response to hypoxic (−19 ± 15 bpm; p = 0.999) and hypercapnic apneas (−14 ± 14 bpm; p = 0.059). Hypercapnic apneas were not different from normocapnic normoxic apneas (p = 0.134). After removal of the normocapnic normoxic heart rate response, the change in heart rate during hypercapnic hypoxia (−11 ± 16 bpm) was similar to the summed change during hypercapnia+hypoxia (−9 ± 10 bpm; p = 0.485). Only hypoxia contributed to this bradycardic response. Under apneic conditions, the cardiac response is driven by 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".