Age‐Related Differences in the Quantitative Echo Texture of the Median Nerve
Bibliographic record
Abstract
OBJECTIVES: Currently, there are no quantitative data on the echo texture of a peripheral nerve. This study was designed to objectively compare the differences in the echo texture of the median nerve in the young and the elderly. METHODS: The median nerves of 10 healthy young volunteers (<30 years old; group Y) and 10 elderly patients undergoing lower limb surgery (>60 years old; group E) were scanned at the mid forearm by a standardized protocol. The echo texture of a normalized median nerve image was analyzed for the echo intensity and spatial distribution of pixels. Noise in the image was reduced by using a median filter, and thresholding was performed thereafter. In the resultant binary image, the cross-sectional area, echo intensity, white area index, and black area index of the median nerve were determined by computerized texture analysis. RESULTS: The mean cross-sectional area of the median nerve in group E was significantly smaller than that in group Y (P = .002). The mean echo intensity and white area index in group E were significantly higher than those in group Y (P= .002 and .012). The mean black area index in group E was correspondingly significantly lower than that in group Y (P = .012). In group Y, the mean white area index was significantly lower than the black area index (P = .006) but not in group E (P = .213). CONCLUSIONS: There are significant differences in the echo texture of the median nerve between the young and the elderly. These differences may be due to age-related changes in the relative proportion of neural fascicles and connective tissue within the nerve.
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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) | 0.001 | 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".