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Record W4391485481 · doi:10.1097/gox.0000000000005570

Tips, Tricks, and Pearls for a Superior Patient and Surgeon Experience for Wide-awake Dupuytren Surgery

2024· article· en· W4391485481 on OpenAlexaff
Natasha Barone, Donald H. Lalonde, Jean Paul Brutus

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2024
Typearticle
Languageen
FieldMedicine
TopicDupuytren's Contracture and Treatments
Canadian institutionsSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsTourniquetMedicineSurgeryLocal anesthesiaGeneral surgery

Abstract

fetched live from OpenAlex

Fasciectomy for Dupuytren disease is a common procedure traditionally performed with a tourniquet under general or regional anesthesia. Since the year 2001, the wide-awake local anesthesia no tourniquet (WALANT) approach has been applied successfully to Dupuytren surgery, with current excellent surgeon and patient satisfaction. However, using WALANT for Dupuytren surgery may be intimidating for hand surgeons who want to begin using this method. The purpose of this article is to offer a series of tips and tricks the authors have learned after having performed hundreds of WALANT fasciectomies, to make this technique easier for surgeons and a more pleasurable experience for patients.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.004

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2024
Admission routes1
Has abstractyes

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