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Record W4200073816 · doi:10.1051/medsci/2021186

Extraction automatique de repères vertébraux à partir d’échographies

2021· article· fr· W4200073816 on OpenAlexaff
Arnaud Brignol, Farida Chériet, Catherine Laporte

Bibliographic record

Venuemédecine/sciences · 2021
Typearticle
Languagefr
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsÉcole de Technologie SupérieurePolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPhilosophyHumanitiesPhysics

Abstract

fetched live from OpenAlex

Certaines formes de myopathies telles que la dystrophie musculaire de Duchenne entraînent une dégénérescence progressive des muscles chez le patient. Ceci se traduit par l’apparition d’une scoliose dont la gravité augmente au cours du temps. La norme clinique pour le suivi de la scoliose consiste à réaliser un examen radiographique. Malheureusement, l’exposition répétée aux rayons X est nocive pour la santé du patient. L’échographie est une technique d’imagerie médicale non irradiante qui utilise des ondes ultrasonores (US). Cependant, l’interprétation des échographies de vertèbres est souvent difficile en raison de la qualité variable des images. En réponse à ce défi, nous présentons une méthode pour localiser automatiquement les vertèbres sur les échographies. La validation de cette approche reproductible laisse à penser qu’il serait possible, à terme, de remplacer une partie des examens radiographiques standards par l’échographie.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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.

Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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