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Record W2222451732 · doi:10.1007/s00167-015-3972-2

A cadaveric assessment of the risk of nerve injury during open subpectoral biceps tenodesis using a bicortical guidewire

2016· article· en· W2222451732 on OpenAlexafffund
Adnan Saithna, Alison Longo, Robert W. Jordan, Jeff Leiter, Peter MacDonald, Jason Old

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2016
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsUniversity of ManitobaPan Am Clinic
FundersPan Am Clinic Foundation
KeywordsCadaveric spasmMedicineCadaverBicepsAxillary nerveDissection (medical)CalipersRadial nerveAnatomyNerve injuryMusculocutaneous nerveSurgeryPosterior interosseous nerveIatrogenic injuryBrachial plexusPalsy

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the risk of neurological injury from the placement of a bicortical guidewire during subpectoral biceps tenodesis. METHODS: Ten forequarter cadaver specimens were evaluated. A bicortical guidewire was placed, and measurements to important local neurological structures were made with digital calipers at open dissection. RESULTS: The mean (range, SD) distances from the guidewire to the respective nerves was as follows: axillary nerve posteriorly, 15.7 mm (10-22 mm, 3.4); axillary nerve laterally, 18.7 mm (12-27 mm, 4.3); radial nerve posteriorly, 26.2 mm (16-35 mm, 7.0); radial nerve medially, 25 mm (16-33 mm, 4.4); and musculocutaneous nerve, 20.1 mm (12-26 mm, 5.2). CONCLUSIONS: There has been some disagreement in the literature regarding the proximity of a bicortical guidewire to the axillary nerve posteriorly. The results of this study concur with reports from several other authors and demonstrate that this nerve is at risk of iatrogenic injury when using this technique. The clinical relevance of this work is to allow surgeons to better understand the proximity of the nerve to a bicortical guidewire and to highlight that this risk is avoided with a unicortical technique.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.322
Teacher spread0.291 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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