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Record W4322700631 · doi:10.1177/229255031402200105

Double free gracilis muscle transfer after complete brachial plexus injury: First Canadian experience

2014· article· en· W4322700631 on OpenAlexaffabout
Kate Elzinga, Kevin J. Zuo, Jaret L. Olson, Michael Morhart, Sasha Babicki, K. Ming Chan

Bibliographic record

VenuePlastic Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBrachial plexusMedicineGracilis muscleBrachial plexus injuryElbowAnatomyAvulsionSurgeryWrist

Abstract

fetched live from OpenAlex

Traumatic brachial plexus root avulsions are devastating injuries, and are complex and challenging to reconstruct. Double free muscle transfer using the gracilis muscles is a potentially effective method of restoring upper extremity function. The authors report on the first two patients treated using this technique in Canada. Both sustained traumatic brachial plexus root avulsion injuries resulting in a flail arm. In the first step of this two-stage procedure, a gracilis muscle was transferred to restore elbow flexion, and wrist and digit extension. Months later, the transfer of the second gracilis muscle was performed to enhance elbow flexion and to enable wrist and digit flexion. Postoperatively, both patients achieved Medical Research Council grade 4 elbow flexion, functional handgrip and were able to return to gainful employment. Patient satisfaction was high and active range of motion improved substantially. The authors' experience supports the use of this technique following severe brachial plexus injury.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.866

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.241
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations14
Published2014
Admission routes2
Has abstractyes

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