Cross‐sectional area reference values for peripheral nerve ultrasound in adults: A systematic review and meta‐analysis—Part III: Cervical nerve roots and vagal nerve
Bibliographic record
Abstract
Abstract Background and purpose Measurement of the cross‐sectional area (CSA) of cervical nerve roots using ultrasound is useful in the evaluation of inflammatory polyneuropathies, and measurement of CSA of the vagal nerve might give information about involvement of the autonomic nervous system. We performed a systematic review and meta‐analysis of published CSA reference values for cervical nerve roots and vagal nerve. Methods We included available‐to‐date nerve ultrasound studies on healthy adults and provide meta‐analysis for CSA of the following nerves: cervical nerve roots C5, C6, and C7 as well as vagal nerve in the carotid sheath at the carotid bifurcation level. We report regression and correlation analyses for age, gender, height, weight, and geographic continent. Results We included 11 studies with 885 healthy volunteers (mean age = 42.7 years) and 3149 examined nerve sites. Calculated mean pooled CSA of C5 root was 5.6 mm 2 (95% confidence interval [CI] = 4.6–6.7 mm 2 , n = 911), of C6 root was 8.8 mm 2 (95% CI = 7.4–10.3 mm 2 , n = 909), of C7 root was 9.5 mm 2 (95% CI = 8.0–10.9 mm 2 , n = 909), and of vagal nerve was 2.2 mm 2 (95% CI = 1.5–2.9 mm 2 , n = 420). No heterogeneity was found across studies for any site. Subgroup analysis revealed no significant effects of age, gender, height, weight, and geographic continent on CSA for any of these nerve sites. Conclusions We provide the first meta‐analysis on CSA reference values for the cervical nerve roots and the vagal nerve, with no heterogeneity of reported CSA values at all nerve sites. Our data facilitate the goal of an international standardized evaluation protocol.
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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".