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Record W2312547931 · doi:10.1097/scs.0b013e31822e8045

Application of CAD/CAM Prefabricated Age-Matched Templates in Cranio-Orbital Remodeling in Craniosynostosis

2011· article· en· W2312547931 on OpenAlexaff
Jonathan Burge, Nikoo R. Saber, Thomas Looi, Brooke French, Zoha Usmani, Niloofar Anooshiravani, Peter Kim, Christopher R. Forrest, John H. Phillips

Bibliographic record

VenueJournal of Craniofacial Surgery · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineCraniosynostosisForeheadCraniofacialCADRapid prototypingOrthodonticsCoronal planeComputer-aided manufacturingFacial traumaEngineering drawingSurgeryRadiology

Abstract

fetched live from OpenAlex

Infants with craniosynostosis involving the metopic and coronal sutures require cranio-orbital reshaping to correct craniofacial dysmorphologic feature and to improve facial balance. Currently, surgical techniques to create a balanced fronto-orbital region are based on the surgeon's subjective approach and artistic vision in creating a normal shape to the forehead. To date, the use of age-matched templates and computer-assisted design/computer-assisted manufacturing techniques in optimizing the outcomes of surgical intervention in this area have not been explored. The aim of this article was to describe the process of template generation and application based on age-matched controls using computer-assisted design/computer-assisted manufacturing technology and to present this application in 2 cases.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.249
Teacher spread0.226 · 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

Citations64
Published2011
Admission routes1
Has abstractyes

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