Diploic venous anatomy studied in‐vivo by MRI
Bibliographic record
Abstract
Calvarial diploic venous anatomy has been studied post-mortem, but few studies have addressed these venous structures in-vivo. Previous work in our laboratory has shown that intraosseous infusion through the skull diploic space near the diploic veins in animals and humans does access the superior sagittal sinus and the systemic venous system. We developed a volumetric method of imaging the diploic veins in-vivo using MRI, intravenous gadolinium, and digital subtraction to provide for three-dimensional depiction and exact localization of these veins. We hypothesized that this technique would allow for an assessment of the probability of existence, distribution, and concentration of diploic veins in the skull. We scanned 31 neurosurgical patients, and were able to create 3D diploic venous maps in 74% of them. These maps were processed using Adobe Photoshop CS2. Mathworks MatLab 6.5, once customized, counted the number of pixels occupied by the diploic veins in the processed image. The probability of veins was highest in the occipital regions (100%). The inferior occipital (4.1%) and posterior parietal (4.1%) regions had the highest concentrations of diploic veins. Digital subtraction venography using a volumetric MRI sequence can demonstrate the diploic veins in-vivo. The inferior occipital region may be the best area for an intraosseous infusion device because it has the greatest likelihood of containing a vein and also has the highest concentration of veins.
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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 | 0.001 | 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) | 0.002 | 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".