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Evaluation of the Sensory Deficit after Sural Nerve Harvesting in Pediatric Patients

2007· article· en· W2022473734 on OpenAlexaff
Oren Lapid, Emily S. Ho, Cristina Goia, Howard M. Clarke

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsInstitute of Population and Public HealthPopulation Health Research Institute
Fundersnot available
KeywordsMedicineSural nerveSensory systemSensory nervePhysical medicine and rehabilitationSurgeryNeuroscience

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.272
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2007
Admission routes1
Has abstractyes

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