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Record W2594001514 · doi:10.1371/journal.pone.0173111

Accuracy and reproducibility of virtual cutting guides and 3D-navigation for osteotomies of the mandible and maxilla

2017· article· en· W2594001514 on OpenAlexafffund
Jonathan M. Bernstein, Michael J. Daly, Harley Chan, Jimmy Qiu, David P. Goldstein, Nidal Muhanna, John R. de Almeida, Jonathan C. Irish

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicineMaxillaMandible (arthropod mouthpart)OrthodonticsOrthognathic surgery3d printedOsteotomy3d modelDentistryBiomedical engineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: We set out to determine the accuracy of 3D-navigated mandibular and maxillary osteotomies with the ultimate aim to integrate virtual cutting guides and 3D-navigation into ablative and reconstructive head and neck surgery. METHODS: Four surgeons (two attending, two clinical fellows) completed 224 unnavigated and 224 3D-navigated osteotomies on anatomical models according to preoperative 3D plans. The osteotomized bones were scanned and analyzed. RESULTS: Median distance from the virtual plan was 2.1 mm unnavigated (IQR 2.6 mm, ≥3 mm in 33%) and 1.2 mm 3D-navigated (IQR 1.1 mm, ≥3 mm in 6%) (P<0.0001); median pitch was 4.5° unnavigated (IQR 7.1°) and 3.5° 3D-navigated (IQR 4.0°) (P<0.0001); median roll was 7.4° unnavigated (IQR 8.5°) and 2.6° 3D-navigated (IQR 3.8°) (P<0.0001). CONCLUSION: 3D-rendering enables osteotomy navigation. 3 mm is an appropriate planning distance. The next steps are translating virtual cutting guides to free bone flap reconstruction and clinical use.

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)0.004
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 score0.095
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.115
GPT teacher head0.332
Teacher spread0.218 · 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

Citations44
Published2017
Admission routes2
Has abstractyes

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