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Record W2091760420 · doi:10.1080/10255842.2010.522185

Analysis of humeral head displacements from sequences of biplanar X-rays: repeatability study and preliminary results in healthy subjects

2011· article· en· W2091760420 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
TopicShoulder Injury and Treatment
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsRepeatabilityKinematicsNuclear medicineLandmarkMedicineRadiographyOrthodonticsBiomedical engineeringMathematicsAnatomyComputer scienceRadiologyComputer visionPhysics

Abstract

fetched live from OpenAlex

This work presents an accurate method to measure gleno-humeral translations in a controlled pseudo-kinematic environment. Low-dose biplanar X-rays were acquired from nine healthy subjects at three elevations of the arm in the scapular plane. On each set of images, shoulder bony landmarks were manually located in 3D using a dedicated software. Intra-observer and inter-observer repeatability of landmark identification, as well as humeral head center (GH) translations, were studied. Repeatability for the identification of GH in the global coordinate system (CS) was good with 95% confidence intervals (CIs) ranging from 0.57 to 2.25 mm. Scapular landmark CIs ranged from 0.80 to 12 mm. Gleno-humeral translations of small amplitude ( < 6 mm) were detected in seven out of nine subjects. The results obtained here confirm that calibrated low-dose stereo-radiography is a promising tool for the functional analysis of the shoulder.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.072
GPT teacher head0.390
Teacher spread0.318 · 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