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Record W1501299695 · doi:10.1186/1748-7161-10-s1-o22

Interdependency between corrections in the three anatomic planes in AIS instrumentation

2015· article· en· W1501299695 on OpenAlexaff
Xiaoyu Wang, Laure Boyer, Franck LeNaveaux, Hubert Labelle, Stefan Parent, Carl‐Éric Aubin

Bibliographic record

VenueScoliosis · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineInstrumentation (computer programming)KyphosisRotation (mathematics)Cobb angleOrthodonticsAnatomyNuclear medicineScoliosisRadiographyRadiologySurgeryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A validated patient-specific biomechanical modeling and simulation technique was used to assess the 3D correction of a Lenke-1 AIS case through posterior spinal instrumentation. Uniplanar pedicle screws were bilaterally placed at the 2 proximal, 2 distal, and 3 apical levels. The simulation steps included only the concave side 5.5 mm Cobalt-Chrome rod attachment and its derotation followed by apical vertebral derotation. Eighteen instrumentation simulations were performed with different rod contours (curvatures of 10, 20 and 30 degrees), rod derotation (70, 90 and 110 degrees), and vertebral derotation torques (3 and 5 Nm per screw at the 3 apical levels). Indices in the 3 planes (Cobb angle, thoracic kyphosis, apical vertebral rotation (AVR)) were computed for each simulation. For the eighteen simulations, the coronal plane correction through the concave side rod attachment and derotation was accompanied by an increase of the AVR from 10 to 15 degrees. The increase of thoracic kyphosis was proportional to the preoperative rod curvature and the rod derotation angle. AVR correction through apical vertebral derotation was accompanied by a slight loss of thoracic kyphosis and small improvement of coronal plane correction.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.057
GPT teacher head0.350
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2015
Admission routes1
Has abstractyes

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