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Record W1979378926 · doi:10.1002/jcu.21982

Minimally invasive ultrasound‐guided carpal tunnel release: A cadaver study

2012· article· en· W1979378926 on OpenAlexaff
Javier de la Fuente, Maria Isabel Miguel‐Perez, Ramón Balius, V. higueras guerrero, Johann Michaud, David Bong

Bibliographic record

VenueJournal of Clinical Ultrasound · 2012
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineRetinaculumCarpal tunnel syndromeNeurovascular bundleCadaverCarpal tunnelUltrasoundDissection (medical)SurgeryEndoscopic carpal tunnel releaseCarpal tunnel releaseCadaveric spasmRadiologyWrist

Abstract

fetched live from OpenAlex

BACKGROUND: Carpal tunnel syndrome is a common condition frequently requiring surgical intervention. We describe a new minimally invasive surgical technique for carpal tunnel release utilizing ultrasound (US) visualization. METHODS: The technique was performed on 20 fresh frozen cadaver specimens. A surgical metallic probe with a "U"-shaped trough and upward curved distal tip was precisely positioned in the carpal tunnel with US guidance followed by division of the flexor retinaculum (FR) with a "V"-shaped scalpel. RESULTS: Complete division of the FR was confirmed by US. Dissection performed on the specimens confirmed complete release of FR and absence of neurovascular injury. The distance from the division of the FR to these structures, the "safety margins," was measured. CONCLUSIONS: This new technique for carpal tunnel release appears to combine the safety and efficacy of open carpal tunnel surgery with the advantages of the minimally invasive techniques.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.078
GPT teacher head0.406
Teacher spread0.328 · 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 designObservational
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

Citations24
Published2012
Admission routes1
Has abstractyes

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