Relationship Between Cerebral Blood Flow and Blood Pressure in Long-Term Heart Transplant Recipients
Bibliographic record
Abstract
Heart transplant recipients are at an increased risk for cerebral hemorrhage and ischemic stroke; yet, the exact mechanism for this derangement remains unclear. We hypothesized that alterations in cerebrovascular regulation is principally involved. To test this hypothesis, we studied cerebral pressure-flow dynamics in 8 clinically stable male heart transplant recipients (62±8 years of age and 9±7 years post transplant, mean±SD), 9 male age-matched controls (63±8 years), and 10 male donor controls (27±5 years). To increase blood pressure variability and improve assessment of the pressure-flow dynamics, subjects performed squat-stand maneuvers at 0.05 and 0.10 Hz. Beat-to-beat blood pressure, middle cerebral artery velocity, and end-tidal carbon dioxide were continuously measured during 5 minutes of seated rest and throughout the squat-stand maneuvers. Cardiac baroreceptor sensitivity gain and cerebral pressure-flow responses were assessed with linear transfer function analysis. Heart transplant recipients had reductions in R-R interval power and baroreceptor sensitivity low frequency gain (P<0.01) compared with both control groups; however, these changes were unrelated to transfer function metrics. Thus, in contrast to our hypothesis, the increased risk of cerebrovascular complication after heart transplantation does not seem to be related to alterations in cerebral pressure-flow dynamics. Future research is, therefore, warranted.
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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".