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Record W2041571426 · doi:10.1080/10255842.2010.540758

Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays

2011· article· en· W2041571426 on OpenAlex

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

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2011
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsCentre Hospitalier de l’Université de MontréalÉcole de Technologie Supérieure
Fundersnot available
KeywordsPelvisReproducibilityNuclear medicineTorsion (gastropod)Position (finance)MedicineAnatomyMathematics

Abstract

fetched live from OpenAlex

In clinical routine, lower limb analysis relies on conventional X-ray (2D view) or computerised tomography (CT) Scan (lying position). However, these methods do not allow 3D analysis in standing position. The aim of this study is to propose a fast and accurate 3D-reconstruction-method based on parametric models and statistical inferences from biplanar X-rays with clinical measurements' (CM) assessment in standing position for a clinical routine use. For the reproducibility study, the 95% CI was under 2.7° for all lower limbs' angular measurements except for tibial torsion, femoral torsion and tibiofemoral rotation ( < 5°). The 95% CI were under 2.5 mm for lower limbs' lengths and 1.5 to 3° for the pelvis' CM. Comparisons between X-rays and CT-scan based 3D shapes in vitro showed mean differences of 1.0 mm (95% CI = 2.4 mm). Comparisons of 2D lower limbs' and 3D pelvis' CM between standing 'Shifted-Feet' and 'Non-Shifted-Feet' position showed means differences of 0.0 to 1.4°. Significant differences were found only for pelvic obliquity and rotation. The reconstruction time was about 5 min.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.955
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.109
GPT teacher head0.358
Teacher spread0.249 · 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