MétaCan
Menu
Back to cohort

A new X-ray calibration / reconstruction system for 3D clinical assessment of spinal deformities

2002· article· en· W88594702 on OpenAlexaff
Farida Chériet, Lakhdar Remaki, Christian Bellefleur, Alexander Koller, Hubert Labelle, J. Dansereau

Bibliographic record

VenueStudies in health technology and informatics · 2002
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCalibrationComputer science3D reconstructionDisplacement (psychology)ScoliosisObject (grammar)MedicineComputer visionSet (abstract data type)Constraint (computer-aided design)Artificial intelligenceMathematicsSurgeryGeometryPsychology

Abstract

fetched live from OpenAlex

The main objective of this study was to develop a 3D X-ray reconstruction system of the spine and rib cage for an accurate clinical assessment of spinal deformities. The proposed system uses an explicit calibration technique and a new calibration object composed of: (1) a set of radiopaque markers embedded in a jacket worn by the patient during the X-ray exposures; (2) six control markers to define a reference vertical plane. Computer simulations were performed to evaluate the accuracy of the 3D reconstruction procedure when different kind of displacements were applied on a reference model. Clinical indices computed from the 3D X-ray reconstruction of the spine for 24 scoliotic subjects were compared to those obtained with the DLT method. The results of the evaluation study showed that the new system allows the patient to adopt a normal attitude without any constraint, compensating for its displacement between exposures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.134
GPT teacher head0.446
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueStudies in health technology and informaticsSame topicScoliosis diagnosis and treatmentFrench-language works237,207