Repeatability of ultrasonographic median nerve measures
Bibliographic record
Abstract
In this study we investigated the reliability of ultrasound in measuring median nerve characteristics including cross-sectional area (CSA), flattening ratio (FR), swelling ratio (SR), and mean grayscale. Generalizability theory was used to assess inter- and intrarater reliability using the dependability coefficient (phi), normalized standard error of measurement, and normalized minimum detectable change (MDC(NORM)) for multiple study design protocols. Interrater reliability was generally moderate. Intrarater reliability was mostly good (phi > 0.876) when using a single image, captured on one occasion, and being read once. Intrarater MDC(NORM) ranged from 3.8% to 6.2% for all CSA measures and SR. Using multiple images and/or readings at multiple occasions did not appreciably improve reliability measures. Ultrasound is a reliable tool for measuring median nerve characteristics. We recommend that a single evaluator capture all images for protocols aimed at quantifying median nerve ultrasound measures. We believe an appropriately designed protocol can utilize ultrasound to accurately assess changes in median nerve characteristics after activity.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".