MétaCan
Menu
Back to cohort
Record W2011806877 · doi:10.1055/s-2007-984417

Cross-Sectional Imaging of Internal Derangement of the Wrist with Arthroscopic Correlation

2007· article· en· W2011806877 on OpenAlexaff
Viviane Khoury, Patrick G. Harris, Étienne Cardinal

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsHôpital Saint-LucCentre Hospitalier de l’Université de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineArthroscopyWristWrist arthroscopyMagnetic resonance imagingRadiologyRadiographyPhysical examinationWrist pain

Abstract

fetched live from OpenAlex

Wrist arthroscopy has become an indispensable tool for the surgeon treating internal derangement of the wrist. The role of arthroscopy in both the diagnosis and treatment of intrinsic ligaments and triangular fibrocartilage complex (TFCC) pathology is well established. Arthroscopy remains a surgical procedure with potential complications, and it does not obviate the need for a careful history, physical examination, and conventional radiography. When the diagnosis remains unclear after these initial investigations, cross-sectional imaging studies play a valuable role in the assessment of internal derangement of the wrist. These studies include magnetic resonance imaging (MRI), magnetic resonance arthrography (MRA), and computed tomography arthrography (CTA), the choice of which depends on the specific clinical query. The radiologist must have exact knowledge of the performance of each diagnostic test to select the appropriate one and interpret it in a clinically relevant manner. With continued refinements in the technological aspects of cross-sectional imaging, its potential to replace diagnostic arthroscopy will surely be realized in the near future. This article focuses on the role of cross-sectional imaging and arthroscopy in the evaluation and management of wrist internal derangement, namely of intrinsic ligaments and TFCC pathology.

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 score0.016
Threshold uncertainty score0.230

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 studies0.0000.001
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 head0.006
GPT teacher head0.294
Teacher spread0.288 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSeminars in Musculoskeletal RadiologySame topicOrthopedic Surgery and RehabilitationFrench-language works237,207