MétaCan
Menu
Back to cohort
Record W6921717453 · doi:10.7939/r3-an2y-1k16

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

2023· dissertation· en· W6921717453 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2023
Typedissertation
Languageen
FieldMathematics
TopicHistory and Theory of Mathematics
Canadian institutionsnot available
Fundersnot available
KeywordsEpineurial repairNerve injuryMedian nervePeripheral nerveElectrophysiologyRadial nerveUlnar nerveNerve conduction

Abstract

fetched live from 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.046
GPT teacher head0.274
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Alberta LibrarySame topicHistory and Theory of MathematicsFrench-language works237,207