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Record W4367041995 · doi:10.1097/prs.0000000000010047

Recent Advances in the Treatment of Brachial Plexus Birth Injury

2023· review· en· W4367041995 on OpenAlexaff
Kevin J. Zuo, Emily S. Ho, Sevan Hopyan, Howard M. Clarke, Kristen M. Davidge

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicineBrachial plexusBrachial plexus injuryBirth injuryUpper limbSurgeryPopulationPhysical medicine and rehabilitationPhysical therapyPregnancy

Abstract

fetched live from OpenAlex

LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Describe methods of clinical evaluation for neurologic recovery in brachial plexus birth injury. 2. Understand the role of different diagnostic imaging modalities to evaluate the upper limb. 3. List nonsurgical strategies and surgical procedures to manage shoulder abnormality. 4. Explain the advantages and disadvantages of microsurgical nerve reconstruction and distal nerve transfers in brachial plexus birth injury. 5. Recognize the prevalence of pain in this population and the need for greater sensory outcomes evaluation. SUMMARY: Brachial plexus birth injury (BPBI) results from closed traction injury to the brachial plexus in the neck during an infant's vertex passage through the birth canal. Although spontaneous upper limb recovery occurs in most instances of BPBI, some infants do not demonstrate adequate motor recovery within an acceptable timeline and require surgical intervention to restore upper limb function. This article reviews major advances in the management of BPBI in the past decade that include improved understanding of shoulder pathology and its impact on observed motor recovery, novel surgical techniques, new insights in sensory function and pain, and global efforts to develop standardized outcomes assessment scales.

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)0.002
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 score0.988
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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