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Attention to Detail: The Effect of Fluoroscopic Parallax on Limb Alignment Assessment During Corrective Osteotomy

2023· article· en· W4320709641 on OpenAlexaff
Matthew Weldon, Abraham J. Arenas, Alex Abraham, Layla A. Haidar, Ryan J. Warth, Alfred Mansour

Bibliographic record

VenueJAAOS Global Research and Reviews · 2023
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsParallaxOsteotomyComputer scienceMedicineFluoroscopyOrthodonticsComputer visionArtificial intelligencePhysical medicine and rehabilitationRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: The accuracy of a corrective osteotomy is dependent on many factors. One error rarely considered is using noncentered fluoroscopic imaging to assess intraoperative alignment. This study quantified the coronal alignment error produced by visual parallax per interval changes in vertical and horizontal positioning of the C-arm and alignment rod during intraoperative evaluation. METHODS: Unilateral hip, ankle, and knee fluoroscopic images were obtained from a single intact cadaveric specimen. A center-center fluoroscopic image was obtained by moving the C-arm appeared in the center square of the nine-box grid. With the base of the C-arm stationary, the radiograph generator/intensifier portion of the C-arm was translated medially until the target bone appeared on the edge of the fluoroscopic image. RESULTS: One hundred eight images were obtained. Measurement error increased by an average of 14% per 10 mm of horizontal C-arm offset. Minimal effect was seen if the obtained image was within 5 mm of the true center; however, once 55 mm of offset was reached, all experimental conditions resulted in at least 10 mm of parallax error. CONCLUSION: Our results demonstrate that small variations in C-arm positioning can create statistically significant inaccuracies when assessing limb alignment using intraoperative fluoroscopy.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.134
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.050
GPT teacher head0.434
Teacher spread0.384 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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