Local blood flow in peripheral nerves and their ganglia: Resurrecting key ideas around its measurement and significance
Bibliographic record
Abstract
Over 3 decades ago, seminal work by Phillip Low and colleagues established exquisite physiology around the measurement of nerve blood flow (NBF). Although not widely explored recently, its connection to the clinic has awaited human methodology. While human studies have not achieved a convincing level of rigour, newer imaging technologies are offering early information. The peripheral nerve trunk has parallel blood flow compartments that include epineurial flow dominated by arteriovenous shunts and downstream endoneurial blood flow (EBF). NBF and EBF have lower values than central nervous system blood flow, lack autoregulation yet have sympathetic and peptidergic neurovascular control. Contrary to expectation, injury to nerves is often associated with rises in NBF rather than ischemia, a finding of biological interest corroborated by human studies. Despite its potential importance, quantitative human measurements of EBF and NBF are not yet available. However, with development, careful NBF analysis may present new insights into nerve disorders. Muscle Nerve 57: 884-895, 2018.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".