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Enregistrement W6921717453 · doi:10.7939/r3-an2y-1k16

End-to-Side Nerve Transfer: An Evaluation of Its Efficacy and Functional Impact

2023· dissertation· en· W6921717453 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2023
Typedissertation
Langueen
DomaineMathematics
ThématiqueHistory and Theory of Mathematics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEpineurial repairNerve injuryMedian nervePeripheral nerveElectrophysiologyRadial nerveUlnar nerveNerve conduction

Résumé

récupéré en direct d'OpenAlex

Background Peripheral nerve injury is common, effecting 3% of the population. While surgery can be effective in moderate cases, complete neurologic and functional recovery are often not possible in severe cases of proximal nerve injury. Poor outcomes are attributable to the long-distance nerves must regenerate to reach their targets. End-to-end (ETE) nerve transfer surgery can shorten the distance of regeneration by bridging a dispensable donor nerve to the end of the injured nerve that is closer to the denervated target. Unfortunately, these procedures involve cutting the injured nerve, preventing the possibility for native nerve regeneration, and making in unfeasible for incomplete injuries. Reverse end-to-side (RETS) nerve transfers is an increasingly utilized technique that involves connecting the donor nerve to the side of the injured nerve, which preserves the injured nerve continuity, and potentially allows for donor nerve (1) axonal crossover and the (2) babysitting effect. However, the source of regenerating nerve fibres in the RETS transfer has been inconsistent with some studies that show benefits and others that did not find efficacy in the surgery. Objective To evaluate the amount of (1) axonal crossover from the donor nerve in the RETS transfer using a novel electrophysiology technique. To evaluate the (2) babysitting effect by comparing the RETS transfer to a decompression surgery. Aim 1 — A novel electrophysiological technique to quantify axonal crossover. Seven Martin-Gruber anastomosis (MGA) and nine anterior interosseous nerve (AIN) to ulnar nerve ETE nerve transfer patients were recruited. Motor nerve conduction studies were performed, and the novel digital subtraction technique was compared against the collision technique and innervation ratio method, previous techniques to measure crossover. The digital subtraction method was highly correlated with the collision technique and has several practical advantages. With the increasing use of nerve transfer surgery in severe high ulnar nerve injury, this could be a useful method to identify the presence of MGA prior to surgery and for evaluating nerve recovery following surgery. Aim 2 — A prospective clinical trial comparing RETS with ETE and decompression surgery. Sixty-two subjects (RETS=25 | ETE=16 | decompression=21) from four centres in Western Canada were enrolled. All subjects with severe ulnar nerve injury had nerve compression at the elbow except 10 in the ETE group had nerve laceration or traction injury. The novel digital subtraction technique was used to quantify the regeneration of AIN and ulnar nerve fibers while functional recovery was evaluated using key pinch and Semmes-Weinstein monofilaments. The subjects were followed post-surgically for 3 years. Post-surgically, no reinnervation from the AIN to the abductor digiti minimi muscles was seen in any of the RETS subjects. Significance While clinical translation of RETS has been increasing, the results from published clinical trials has been conflicting, in part because crossover regeneration from the donor nerve has never been measured. From applying the novel electrophysiological technique in the multicentre prospective study, we found there was no crossover regeneration in patients that underwent RETS compared to ETE nerve surgery. The extent of reinnervation from RETS surgery was also no different compared to decompression surgery alone. Based on these findings, the justification for the RETS surgical technique needs to be further evaluated.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,408
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,274
Écart entre enseignants0,227 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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