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Record W2582018134 · doi:10.14740/jmc.v8i2.2715

High Median Nerve Palsy Caused by Pseudoaneurysm After Brachial Catheterization: Two Case Reports

2017· article· en· W2582018134 on OpenAlexvenueno aff
Kensuke Ochi, Goken Iwase, Sakiko Mizuno, Noboru Matsumura, Takuji Iwamoto, Itsuo Watanabe, Hiraku Hotta, Ukei Anazawa, Kazuki Sato, Shinichiro Takayama

Bibliographic record

VenueJournal of Medical Cases · 2017
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeurolysisPseudoaneurysmPalsySurgeryHematomaBrachial plexusComplication

Abstract

fetched live from OpenAlex

We here present two very rare cases of high median nerve palsy caused by pseudoaneurysm after brachial catheterization. A 71-year-old woman developed high median nerve palsy 2 weeks after brachial catheterization. She underwent pseudoaneurysm resection together with neurolysis 4 months after the onset of palsy. Surgical findings suggested that her palsy was caused by both severe compression by pseudoaneurysm and adhesion following hematoma after catheterization. Five months after the surgery, she only had slight sensory disturbance. A 48-year-old lady developed high median nerve palsy 1 week after the catheterization. Simple pseudoaneurysm resection was performed 2 weeks after the catheterization. Neurolysis was not performed. Seven months after the surgery, she still had severe sensory disturbance. Our cases suggested importance of secured astriction after catheterization, and recommended surgical procedure for this condition is combination of pseudoaneurysm resection and neurolysis by hand surgeons. J Med Cases. 2017;8(2):33-35 doi: https://doi.org/10.14740/jmc2715w

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)0.001
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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
Published2017
Admission routes1
Has abstractyes

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