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Record W2060046017 · doi:10.1055/s-0033-1357760

Computer-Assisted Percutaneous Scaphoid Fixation: Concepts and Evolution

2013· article· en· W2060046017 on OpenAlexaff
Erin J. Smith, David R. Pichora, Randy E. Ellis

Bibliographic record

VenueJournal of Wrist Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePercutaneousFixation (population genetics)WristScaphoid fractureRadiologySurgery

Abstract

fetched live from OpenAlex

Background The treatment for undisplaced scaphoid waist fractures has evolved from conventional cast immobilization to percutaneous screw insertion. Percutaneous fixation reduces some of the risks of open surgery, but can be technically demanding and carries the risk of radiation exposure. Recently, computer-assisted percutaneous scaphoid fixation (CAPSF) has been gaining interest. Materials and Methods Conventional percutaneous scaphoid fixation is performed under fluoroscopic guidance and involves insertion of a guide wire along the length of the scaphoid to facilitate placement of a cannulated screw. Adapting computer-assisted techniques for scaphoid fixation poses several unique challenges including patient tracking and registration. Results To date, five groups have successfully implemented systems for CAPSF. These systems have implemented wrist immobilization strategies to resolve the issue of patient tracking and have developed unique guidance techniques incorporating 2D fluoroscope, cone-beam CT, and ultrasound, to circumvent patient-based registration. Conclusions Computer-aided percutaneous pinning of scaphoid waist fractures can significantly reduce radiation exposure and has the potential to improve the accuracy of this procedure. This article reviews the rationale for, and the evolution of, CAPSF and describes the key principles of computer-assisted technology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.261
Teacher spread0.246 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

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