A mathematical model of cerebral perfusion subjected to Gz acceleration.
Bibliographic record
Abstract
BACKGROUND: When the human body is exposed to a high gravitational load, the blood supply to the brain is reduced and loss of consciousness may occur. Our goal is to identify the principal mechanical causes of reduced blood supply to the brain during high +Gz. METHODS: We have developed a mathematical model to investigate the influence of Gz on the cerebral circulation. Blood flow is modeled using a one-dimensional flow approximation, in which the cross-sectional area of elastic vessels is determined as a non-linear function of the transmural (blood minus external) pressure. The intracranial vessels are subjected to cerebrospinal fluid pressure (PCSF) which is determined from the condition that the cranial volume is conserved. RESULTS: For a constant pressure difference of 100 mm Hg applied to the arterial and venous ends of the model, blood flow is diminished for +Gz. At approximately +5 G, the blood flow predicted by the model is insufficient to maintain normal functioning of the brain. PCSF is approximately equal to the blood pressure in the large intracranial veins for all values of Gz. Extracranial arteries and the intracranial vessels do not collapse, even when Gz is substantially higher than normal. However, the extracranial veins are collapsed even for moderate +Gz. CONCLUSIONS: Even if cardiac output is maintained at normal levels, cerebral perfusion will fall because of the increasing resistance of the cerebral flow circuit. This increase is largely due to the collapse of the extracranial veins, which begins at moderate Gz and becomes dominant at a Gz of approximately 4.5.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".