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Record W2987118056 · doi:10.1093/jhps/hnz058

Periacetabular osteotomy using an imageless computer-assisted navigation system: a new surgical technique

2019· article· en· W2987118056 on OpenAlex
Jessica Hooper, Rachel Mays, Lazaros Poultsides, Pablo Castañeda, Jeffrey M. Muir, Atul F. Kamath

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

VenueJournal of Hip Preservation Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsIntellijoint Surgical (Canada)
Fundersnot available
KeywordsMedicineComputer-assisted surgeryOsteotomySurgeryComputer science

Abstract

fetched live from OpenAlex

Periacetabular osteotomy (PAO) is an effective surgical treatment for hip dysplasia. The goal of PAO is to reorient the acetabulum to improve joint stability, lessen contact stresses and slow the development of hip arthrosis. During PAO, the acetabulum is repositioned to adequately cover the femoral head. PAO preserves the weight-bearing posterior column of the pelvis, maintains the acetabular blood supply and retains the hip abductor musculature. The surgical technique needed to perform PAO is technically demanding, with correct repositioning of the acetabulum the most important-and challenging-aspect of the procedure. Imageless navigation has proven useful in other technically challenging surgeries, although its use in PAO has not yet been investigated. We have modified the standard technique for PAO to include the use of an imageless navigation system to confirm acetabular fragment position following osteotomy. Here, we describe the surgical technique and discuss the potential of this modified technique to improve patient-related outcomes.

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)0.001
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 score0.188
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.309
Teacher spread0.260 · 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