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Record W4412569496 · doi:10.1115/1.4069208

A Novel Articulating Bone Cutting Tool for Minimally Invasive Craniosynostosis Surgery

2025· article· en· W4412569496 on OpenAlexaff
Emma Stickley, Thomas Looi, Eric Diller, Dale J. Podolsky

Bibliographic record

VenueJournal of Medical Devices · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsCanada Research ChairsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCraniosynostosisInvasive surgeryMedicineSurgery

Abstract

fetched live from OpenAlex

Abstract Craniosynostosis involves early fusion of cranial sutures, resulting in an abnormal head shape and functional issues such as elevated intracranial pressure. Treatment involves surgery to correct the head shape and to allow unrestricted brain growth. This work aims to overcome the restrictions of current instruments by developing a novel articulated bone cutting tool for minimally invasive craniosynostosis surgery. A handheld tendon-driven tool with a bending section was developed to enhance reachability along the skull. The tool comprises a driving unit, a bending section attached to an end-effector, and a flexible endoscope. The prototype was developed and characterized to validate its accuracy, stiffness, bone-cutting capability, and reachability. A prototype was developed with a high reachability of 72.2%, 71.6%, 78.4% (length of the osteotomy/total planned path) using sagittal, metopic, and unicoronal craniosynostosis skull models, respectively. The tool demonstrated low deflection (≤5 mm for all configurations except for 0 deg bending) under 5 N external force and is capable of cutting bone-like materials with varying bending angles (range from 45 deg to 90 deg). These results indicate the potential of the bone-cutting tool to provide a paradigm shift in the treatment of craniosynostosis, expanding the benefits of minimally invasive approach to more patients.

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)0.003
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: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.014
GPT teacher head0.290
Teacher spread0.277 · 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
Published2025
Admission routes1
Has abstractyes

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