MétaCan
Menu
Back to cohort
Record W2589235436 · doi:10.1177/2513826x1500100207

Ulnar Nerve Compression in the Proximal Forearm By An Intraneural Arteriovenous Malformation

2015· article· en· W2589235436 on OpenAlexvenueno aff
Mohd Saquib, Nida Jamil Khan, Mohd. Altaf Mir

Bibliographic record

VenuePlastic Surgery Case Studies · 2015
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsForearmMedicineAnatomyUlnar nerveArteriovenous malformationCompression (physics)Cutaneous nerveUpper limbTendonLesionSurgeryElbow

Abstract

fetched live from OpenAlex

Compression of the ulnar nerve is most commonly caused by ganglions, lipomas, anomalous tendon and muscles, trauma related to an occupation and arthritis in the upper limb; however, nerve compression in the upper limb and, more importantly, in the proximal forearm by a vascular lesion is rare. A rare case of ulnar nerve compression in the proximal forearm by an arteriovenous malformation in a 45-year-old man who presented with six-year history of gradually increasing swelling in medial aspect of proximal forearm is reported. After excision of the tumour, it was found to be an arteriovenous malformation.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
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 head0.077
GPT teacher head0.321
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 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePlastic Surgery Case StudiesSame topicPeripheral Nerve DisordersFrench-language works237,207