Ultrasonographic Evaluation of Morphological Changes in Peripheral Nerves after Traumatic Injury and Nerve Repair - A Prospective Study.
Bibliographic record
Abstract
Purpose Ultrasound (US) has gained in importance for the visualization of morphological changes of injured nerves. After surgical repair, changes in neural structures are seen over time. The correlation of morphologic changes in US with the corresponding nerve function is uncertain. The aim of this study is to determine a correlation of post-traumatic morphological nerve changes with US and with nerve function after surgery. Materials and Methods This dual-center, prospective cohort study was conducted between 2017 and 2022 and included 20 mixed sensory motor nerve lesions. Patients were followed up clinically (sensitivity, pain, and motor function) with US and electroneuromyography. We determined the US changes of the nerves including the interaction of the tissue after nerve repair and any correlation with nerve function. With US nerve cross-sectional area (CSA), the number of traversing fascicles, hypo-echogenicity, and presence of perineural scar were analyzed. Results 20 lesions (12 median and 8 ulnar nerves) of 18 patients with intraoperatively confirmed nerve injury of at least 50% in the forearm were included. The average CSA was over 20 mm 2 throughout the follow-up period, corresponding to a neuroma in continuity compared to the opposite side (10.75 mm 2 ). Sensibility and motor function at 12 months were 6xS3/4 and 10xM3-5. There was a statistically significant correlation between continuous fascicles on US at 6 months and sensitivity at 12 months. Conclusion This study supports the presence of post-traumatic morphological changes in nerve fibers with US after traumatic injury. Morphological changes in nerve structure after trauma can be detected with US indicating a correlation between continuity of nerve fascicles and development of sensitivity and motor function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".