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Record W4403941060 · doi:10.1001/jamaoto.2024.3246

Optimizing Osteotomy Geometries in Posterolateral Mandibulectomies

2024· article· en· W4403941060 on OpenAlexaff
Hugh Andrew Jinwook Kim, Michael J. De Biasio, Vito Forte, Ralph Gilbert, Jonathan C. Irish, David P. Goldstein, John R. de Almeida, Matthew M. Hanasono, Peirong Yu, Douglas B. Chepeha, Thomas Looi, Christopher M. K. L. Yao

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMolarOsteotomyMandible (arthropod mouthpart)Materials scienceMasticatory forceOrthodonticsSagittal planeStress (linguistics)von Mises yield criterionMandibular first molarDentistryMedicineFinite element methodAnatomyBiologyStructural engineering

Abstract

fetched live from OpenAlex

Importance: Reconstructive stability after mandibulectomy with osseous autogenous transplant is influenced by masticatory forces and the resulting stress on the titanium plate. Objective: To determine an optimal geometry of mandibular osteotomy that minimizes undesirable loading of the reconstruction plate. Design, Setting, and Participants: In this combined in silico and in vitro basic science study, segmented computed tomography images of an adult male human mandible downloaded from the Visible Human Project were analyzed. Data were collected from July to November 2023. Exposures: Four posterolateral mandibular resections and bony transplants were modeled following (1) vertical, (2) angled, (3) step, and (4) sagittal osteotomies. Using SOLIDWORKS software, mastication was simulated under (1) incisal, (2) ipsilateral molar, and (3) contralateral molar loading. Mandible models were then 3-dimensionally printed, osteotomized, and plated. Masticatory loads were simulated using pulleys, and strains were measured using strain gauges. Main Outcomes and Measures: On the reconstruction plate, von Mises stresses were measured in silico, and strains were measured using strain gauges in vitro. Stress and strain are reactions of a material to loading that can result in irreversible deformation or fracture. Results: In silico, maximum plate stress was highest with the vertical osteotomy, followed by the angled osteotomy (median difference vs vertical: ipsilateral molar loading, 126 MPa; 95% CI, 18-172; incisal loading, -24 MPa; 95% CI, -89 to 31; contralateral molar loading, 91 MPa; 95% CI, 23-189), step osteotomy (median difference vs angled: ipsilateral molar loading, 168 MPa; 95% CI, 112-235; incisal loading, 80 MPa; 95% CI, 15-140; contralateral molar loading, -17; 95% CI, -115 to 83), and sagittal osteotomy (median difference vs step: ipsilateral molar loading, 122 MPa; 95% CI, 102-154; incisal loading, 197 MPa; 95% CI, 166-230; contralateral molar loading, 161 MPa; 95% CI, 21-232). An angled osteotomy had the lowest stress at 30° of angulation (median difference vs contralateral molar loading at 40° of angulation: 111 MPa; 95% CI, 4-186). In vitro, the vertical osteotomy had the highest maximum strain, followed by the angled osteotomy (mean difference vs vertical: incisal loading, 0.021 mV/V; 95% CI, 0.014-0.027; contralateral molar loading, 0 mV/V; 95% CI, -0.004 to 0.005), step osteotomy (mean difference vs angled: incisal loading, 0.015 mV/V; 95% CI, 0.003-0.028; contralateral molar loading, 0.021 mV/V; 95% CI, 0.016-0.027), and sagittal osteotomy (mean difference vs step: incisal loading, 0.006 mV/V; 95% CI, -0.006 to 0.018; contralateral molar loading, 0.020 mV/V; 95% CI, 0.015-0.026). Conclusions and Relevance: In this study, the traditional vertical osteotomy resulted in less favorable plate stresses in all loading scenarios compared with angled, step, or sagittal osteotomies, in silico and in vitro. Future clinical studies analyzing the impact of varying osteotomy geometries are warranted to translate these findings to the operating room.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.270
Teacher spread0.254 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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