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Record W2875665783 · doi:10.1155/2018/4362367

Imageless Navigation Improves Intraoperative Monitoring of Leg Length Changes during Total Hip Arthroplasty for Legg‐Calve‐Perthes Disease: Two Case Reports

2018· article· en· W2875665783 on OpenAlex
Ritesh R. Shah, Varsha D Gobin, Jeffrey M. Muir

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCase Reports in Orthopedics · 2018
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsIntellijoint Surgical (Canada)University of Waterloo
Fundersnot available
KeywordsLegg-Calve-Perthes diseaseMedicineAvascular necrosisTotal hip arthroplastyFemoral headSurgeryRadiography

Abstract

fetched live from OpenAlex

Legg-Calve-Perthes disease is a rare condition characterized by avascular necrosis and malformation of the femoral head. For many patients, total hip arthroplasty (THA) is the only viable treatment option; however, there are challenges associated with THA in this population, primarily the equalization of leg lengths. Here, we present two cases of Legg-Calve-Perthes disease treated via total hip arthroplasty with the assistance of an imageless, computer-assisted navigation device. In each case, the device provided intraoperative data on leg length in real time, allowing for improved accuracy of component placement. Postoperative leg lengths were confirmed to be equalized in each case using radiographs. These cases are, to our knowledge, the first such cases using imageless navigation during THA and demonstrate the benefits of such assistive technologies in challenging cases such as Legg-Calve-Perthes disease.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.317
Teacher spread0.299 · 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