Bibliographic record
Abstract
Peripheral nerves are living dynamic tissues that thrive on a nutritive blood supply. The vascular supply of the peripheral nerve, termed “vasa nervorum” participates intimately in regenerative events and influences their success. There are important morphological and physiological differences among microvessels that supply nerve trunk, ganglia, and brain. Each entrains and regulates its vascular supply from differing physiological perspectives, depending on their need for metabolic support. Following injury, vasa nervorum alter their behavior in unique ways that reflect their exposure to molecules released within the microenvironment and that offer insights into the repair process. Blood flow and microvessels of intact nerve trunks Nerve trunk blood vessels, or vasa nervorum, are supplied by upstream arterial branches of major limb vessels. Sometimes these arteries and nerves course together as neurovascular bundles. Peripheral nerves also share their abundant blood supply with other structures in limbs such as bone, connective tissue, skin, and muscle. For this reason, major ischemic lesions are likely to target several tissues and cause widespread damage. The redundant and abundant blood suppy of nerve trunks, however, can be advantageous because the interruption of a single artery is unlikely to cause significant ischemia. There are some sites where there is ischemic vulnerability, known as watershed zones. These are found at areas supplied by terminal branches of overlapping arterial trees. For example, a nerve watershed zone has been identified in the proximal tibial nerve of rats [458].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".