Evaluation of the Sensory Deficit after Sural Nerve Harvesting in Pediatric Patients
Bibliographic record
Abstract
BACKGROUND: The sural nerve is a sensory nerve that innervates the proximal part of the lateral aspect of the foot. The sural nerve is often harvested for nerve grafting. Sensory loss in the area supplied by the sural nerve could be expected, causing a lack of protective sensation and a potential risk of injury. The sensory outcome of sural nerve harvesting has not been documented in children. The aim of this study was to evaluate the sensory deficit following sural nerve harvest in infants. METHODS: The authors conducted a controlled study. Evaluation and mapping of the sensory thresholds in the sural nerve distribution were performed using the Semmes-Weinstein monofilament method on four predetermined sites on the foot. A questionnaire was used to elicit subjective findings. The inclusion criteria were children older than 6 years who had undergone bilateral sural nerve harvesting for brachial plexus reconstruction in the first year of life. Normal volunteers served as controls. RESULTS: Fourteen patients and 14 controls were enrolled in the study. Eighty-six percent of the feet that were operated on had a sensory deficit (p = 0.0001). The patients reported no concerns regarding the sensation of their feet. CONCLUSIONS: Sural nerve harvesting in children leaves a measurable sensory deficit; however, this deficit does not seem to have clinical implications for the patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".