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Record W4309664956 · doi:10.2106/jbjs.oa.22.00039

A Corresponding Point Measurement System Provides Reliable Measurement of Displacement for Medial Epicondyle Fractures

2022· article· en· W4309664956 on OpenAlexaff
Ryan Guzek, Kathleen Harwood, David Isaacs, Divya Talwar, Eric W. Edmonds, Peter D. Fabricant, V. Elaine Joughin, Kevin Latz, Stephanie W. Mayer, Scott McKay, Donna Pacicca, Michael G. Saper, J. Todd R. Lawrence

Bibliographic record

VenueJBJS Open Access · 2022
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsDisplacement (psychology)EpicondyleRadiographyOrthodonticsReliability (semiconductor)MedicineNuclear medicineOblique caseInterclass correlationIntraclass correlationMathematicsRadiologyReproducibilityPhysicsStatisticsPsychology

Abstract

fetched live from OpenAlex

Little consensus exists on the best method for evaluation and management of pediatric medial epicondyle fractures because of an inability to reliably evaluate fracture displacement with standard imaging techniques. This study aimed to determine the performance of various radiographic views in evaluating displaced medial epicondyle fractures when using a standardized measurement methodology. Methods: Ten fellowship-trained pediatric orthopaedic surgeons assessed fracture displacement in 6 patients with displaced medial epicondyle fractures using radiographic views (anteroposterior, lateral, axial, internal oblique [IO], and external oblique [EO]) and computed tomographic (CT) views (axial, 3-dimensional [3D] horizontal, and 3D vertical). Raters used a corresponding point method for measuring displacement. For each image, raters measured the absolute displacement, categorized the percent of displacement relative to the size of the fragment and fracture bed, and indicated a treatment option. Interobserver reliability was calculated for each view. Bland-Altman plots were constructed to evaluate the bias between each radiograph and the mean of the CT methods. Results: For absolute displacement, anteroposterior and EO views showed almost perfect interobserver reliability, with an interclass correlation coefficient (ICC) of 0.944 for the anteroposterior view and an ICC of 0.975 for the EO view. The axial view showed substantial reliability (ICC = 0.775). For the displacement category, almost perfect reliability was shown for the anteroposterior view (ICC = 0.821), the axial view (ICC = 0.911), the EO view (ICC = 0.869), and the IO view (ICC = 0.871). Displacement measurements from the anteroposterior, axial, and EO views corresponded to the measurements from the CT views with a mean bias of <1 mm for each view. However, the upper and lower limits of agreement were >5 mm for all views, indicating a substantial discrepancy between radiographic and CT assessments. Treatment recommendations based on CT changed relative to the recommendation made using the anteroposterior view 29% of the time, the EO view 41% of the time, and the axial view 47% of the time. Conclusions: Using a corresponding point measurement system, surgeons can reliably measure and categorize fracture displacement using anteroposterior, EO, and axial radiographic views. CT-based measurements are also reliable. However, although the mean difference between the radiograph-based measurements and the CT-based measurements was only about 1 mm, the discrepancy between radiographic views and CT-based methods could be as large as 5 to 6 mm. Level of Evidence: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

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)0.008
metaresearch head score (Gemma)0.035
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.101
GPT teacher head0.399
Teacher spread0.298 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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