Validation of models of venous outflow from the cranial cavity in the supine and upright body positions
Bibliographic record
Abstract
Introduction: This in silico study was aimed at building reliable models comprising both the jugular and vertebral outflow pathways from the cranial cavity.Such models could be useful in further research on understanding the relevance of stenoses of the internal jugular vein.Material and methods: Flowsquare+ computational flow mechanics software was used.Models of cerebral venous outflow comprised an initial inflow field representing cerebral circulation, which branched into 2 alternative outflow routes: a tube representing the internal jugular vein, and an irregular network representing the vertebral veins and epidural venous plexus.Then these 2 alternative pathways joined again together to form the outflow field.Results: In the model representing open internal jugular vein, most (84%) of the blood flowed out through this vein.The remaining 16% utilized the vertebral pathway, which is in line with observations in living subjects.In this model the total flow volume in outflow field was 399 ml/min, which was also in line with observations in humans.When the modelled internal jugular vein was collapsed, which in living humans is seen in the upright body position, in order to get proper values of the total flow it was necessary to increase the value of flow velocity in the initial field.In such a model the total flow was similar to the former one, but the jugular pathway was responsible for only 36% of the total flow.Conclusions: Models comprising jugular and vertebral outflow pathways have been validated and could be used in future in silico investigations of extracranial disturbances of the cerebral venous outflow.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".