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Record W2415013792

Emerging techniques for nerve repair: nerve transfers and nerve guidance tubes.

2006· article· en· W2415013792 on OpenAlexaff
Rajiv Midha

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsMedicineEpineurial repairPeripheral nervePeripheral nerve injuryNerve injurySurgeryNerve rootNerve repairSpinal cord injuryRegeneration (biology)Spinal cordAnesthesiaAnatomy
DOInot available

Abstract

fetched live from OpenAlex

Peripheral nerve injury is a serious health concern for society, affecting 2.8% of trauma patients, many of whom acquire life-long disability.52 For example, approximately 360,000 people in the United States experience upper extremity paralytic syndromes yearly, resulting in 8,648,000 and 4,916,000 restricted activity days and bed/disability days, respectively.23 Because peripheral neurons spontaneously sprout new axons after injury, patients with milder severity nerve injuries improve spontaneously, but many patients have more severe injuries that have a poor natural history to recover.16 Most severe injuries are associated with nerve injury gaps or lengthy scar within the nerve that prevents regenerating axons from effectively innervating the distal nerve stump.44 These are managed with a nerve repair of the divided nerve or, for the usual scenario of gaps longer than 1 cm or scar segments that need to be resected, placement of interposed nerve grafts.47 The nerve grafts provide a pathway for regenerating axons from the proximal nerve stump to innervate the distal one.45 However, recovery after nerve graft repair is limited by incomplete and non-specific regeneration and variable clinical results.27,43 Based on sound and solid experimental literature over the past half century, peripheral nerve surgeons in the past three decades have been increasingly using alternative techniques to interposed nerve autografts in an attempt to improve outcomes. For lengthy nerve injuries, or for those very proximal ones in which the spinal nerve root has been or are likely avulsed from the spinal cord, the use of nerve transfers has emerged.35 For short injury gaps, surgeons are using nerve guidance tubes in place of nerve grafts to perform the repair.6 This chapter reviews the rationale, principles, and theoretical advantages that these state-of-the-art techniques offer to the surgeon and their patient. Readers are encouraged to read other literature and reviews on each of these topics, which are provided in the references cited at the end of this chapter, for more detailed information as appropriate. NERVE TRANSFERS Nerve transfers, also referred to as “neurotization,” involve the repair of a distal denervated nerve element using a proximal foreign nerve as the donor of neurons and their axons, which will reinnervate the distal targets. The concept is to sacrifice the function of a (lesser valued) donor muscle to revive function in the recipient nerve and muscle that will undergo reinnervation.50 Since their first report by Tuttle64 in 1913 and popularization by Narakas49 three decades ago, nerve transfers have been used increasingly for the repair of brachial plexus injuries, especially in cases in which the proximal motor source of the denervated element is absent because of avulsion from the spinal cord.43 Increasingly advocated are the use of transfers in situations in which the proximal motor source is available, but the regeneration distance is so long that the outcome would be poor. A nerve transfer into the denervated distal nerve stump close to the motor end-organ would then restore function, which would not be possible otherwise.51 The use of nerve transfers has, therefore, been a major advance in the field of brachial plexus nerve reconstructive surgery, with many different ingenious transfers associated with improving results, as reported and reviewed recently.8,21,42,57,62 The anatomic and physiological principles that underlie nerve transfers are relatively straightforward. Because motor recovery has been the main goal, the choice of a donor nerve element that has a reasonable aliquot of motor fibers is required.50 The loss of the muscle denervated by transferring the donor nerve must not represent loss of important or critical function.31 Obviously, the value of the neuromuscular element to be reinnervated must greatly exceed the utility of the sacrificed one. An excellent compromise is achieved if some function of the donor muscle can be retained, by using only a portion of the nerve as the donor, exemplified by the use of only the distal terminal branch of accessory (transferred to suprascapular nerve), thereby sparing proximal branches to trapezius muscle.43 There are several important principles to adopt in order to maximize outcome in nerve transfers, the first of which is to reinnevate the recipient nerve as close to the target muscle as possible.51 An outstanding example of the latter is the Copyright © 2006 by Lippincott Williams & Wilkins 0148-703/06/5301-0185

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.258
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations49
Published2006
Admission routes1
Has abstractyes

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