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Record W168344172 · doi:10.1177/229255030601400410

The Essex-Lopresti Injury: More Than Just a Pain in the Wrist

2006· article· en· W168344172 on OpenAlexaffvenue
Shirley Hutchinson, Kenneth J. Faber, Bing Siang Gan

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsSt Joseph's Health CentreWestern University
Fundersnot available
KeywordsMedicineWristForearmInterosseous membraneWrist painElbowRadial head fractureSurgeryPhysical medicine and rehabilitationPhysical therapyRadial head

Abstract

fetched live from OpenAlex

As the scope of plastic surgical practice expands to include disorders of the carpus and wrist, it has become increasingly important for plastic surgeons to understand pathoanatomy that has not traditionally been considered an integral component of training. The Essex-Lopresti injury consists of a radial head fracture with associated injury to the forearm interosseus membrane and longitudinal instability of the distal radioulnar joint. Early recognition of this disorder usually results in a predictable and satisfactory outcome. However, when this disorder is unrecognized, late reconstruction is challenging and unpredictable, and treatment may be misdirected to the wrist alone if the forearm and elbow are not considered as a component of this injury. The present report describes the importance of examining the elbow in all cases of wrist pain. As well, the literature is reviewed regarding the differences in treatment of acute and chronic Essex-Lopresti injuries. As plastic surgeons become more involved in the treatment of wrist injuries, the conscientious practitioner should be aware of more complicated pathology that may present as a seemingly straightforward wrist problem.

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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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

Citations12
Published2006
Admission routes2
Has abstractyes

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