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Record W4281796022 · doi:10.1055/a-1808-6973

Cubital Tunnel Syndrome: Review of Diagnosis and Management

2022· article· en· W4281796022 on OpenAlexaff
Devin Patrick Ferguson, Ruby Grewal

Bibliographic record

VenueHandchirurgie · Mikrochirurgie · Plastische Chirurgie · 2022
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsHand and Upper Limb Clinic
Fundersnot available
KeywordsMedicineCubital tunnel syndromeSurgical decompressionSurgeryDecompressionCubital tunnelUlnar nerveEntrapment NeuropathyCarpal tunnel syndromeElbow

Abstract

fetched live from OpenAlex

Cubital tunnel syndrome is one of the most common upper extremity nerve entrapment conditions. Clinical examination and electrodiagnostic studies assist in the diagnosis. Mild cases can be successfully treated conservatively, aiming to reduce traction and compression on the nerve. Surgical management of cubital tunnel syndrome is increasing in frequency. Multiple surgical options exist ranging from simple decompression to decompression and anterior transposition of the nerve. There is no preferred surgical technique. It is critical to reduce the risk of recurrent cubital tunnel syndrome. Revision surgical procedures have worse outcomes than primary surgical procedures.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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