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Record W3113865090 · doi:10.1002/rcs.2220

Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models

2021· article· en· W3113865090 on OpenAlex
Andrew Kanawati, Renan Fernandes, Aaron Gee, Jennifer Urquhart, Christopher S. Bailey, Parham Rasoulinejad

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

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2021
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsVictoria HospitalLondon Health Sciences Centre
Fundersnot available
KeywordsCadaveric spasm3d printed3d modelMagnetic resonance imagingLumbarLumbar vertebraeAnatomyNuclear medicineMedicineBiomedical engineeringRadiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: This study will examine the differences between human lumbar vertebrae, three-dimensional (3D) scans of these bones, 3D models based on 'Black-bone' magnetic resonance imaging (MRI) scans, and 3D-printed models. MATERIALS AND METHODS: 3D mesh models were created from the "Black-bone" MRI data from two cadaveric human spines, and then 3D printed. Four models were analysed and compared: anatomic bones, 3D-scanned models, MRI models and 3D-printed models. RESULTS: There was no significant difference between when comparing the average of all measurements between all model types (p = 0.81). The mean dice coefficient was 0.91 (SD 0.016) and the mean Hausdorff distance was 0.37 mm (SD 0.04 mm) when comparing the MRI model to the 3D-scanned model. The mean volumes for the MRI model and the 3D scanned model were 10.42 and 10.04 ml (p = 0.085), respectively. CONCLUSIONS: The 'Black-bone' MRI could be a valid radiation-free alternative to computed tomography for the 3D printing of lumbar spinal biomodels.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.044
GPT teacher head0.275
Teacher spread0.230 · 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