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Record W3155070271 · doi:10.2106/jbjs.20.01716

Prevention and Treatment of Nerve Injuries in Shoulder Arthroplasty

2021· review· en· W3155070271 on OpenAlexaff
Matthew M. Florczynski, Ryan Paul, Timothy Leroux, Heather L. Baltzer

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNeurolysisSurgeryAxillary nerveBrachial plexusReconstructive SurgeonSuprascapular nerveArthroplastyBrachial plexus injuryRadial nerveNerve injuryTendon transferElectromyographyMusculocutaneous nerveTendonPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

➤: Nerve injuries during shoulder arthroplasty have traditionally been considered rare events, but recent electrodiagnostic studies have shown that intraoperative nerve trauma is relatively common. ➤: The brachial plexus and axillary and suprascapular nerves are the most commonly injured neurologic structures, with the radial and musculocutaneous nerves being less common sites of injury. ➤: Specific measures taken during the surgical approach, component implantation, and revision surgery may help to prevent direct nerve injury. Intraoperative positioning maneuvers and arm lengthening warrant consideration to minimize indirect injuries. ➤: Suspected nerve injuries should be investigated with electromyography preferably at 6 weeks and no later than 3 months postoperatively, allowing for primary reconstruction within 3 to 6 months of injury when indicated. Primary reconstructive options include neurolysis, direct nerve repair, nerve grafting, and nerve transfers. ➤: Secondary reconstruction is preferred for injuries presenting >12 months after surgery. Secondary reconstructive options with favorable outcomes include tendon transfers and free functioning muscle transfers.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
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 head0.068
GPT teacher head0.353
Teacher spread0.285 · 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 designOther design
Domainnot available
GenreReview

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

Citations11
Published2021
Admission routes1
Has abstractyes

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