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113. Development, Optimization and Characterization of 3-mm Diameter Robotic Instruments for Robotic Assisted Cleft Palate Repair

2025· article· en· W4409786557 on OpenAlexaff
Glenn Maguire, Ethan Tang, Thomas Looi, Dale J. Podolsky

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCharacterization (materials science)Materials scienceComputer scienceBiomedical engineeringEngineeringNanotechnology

Abstract

fetched live from OpenAlex

PURPOSE: Cleft palate surgery involves dissection of delicate tissue within the confines of the infant oral cavity with reduced visualization, access and manipulability of standard instruments. Robotic assisted cleft palate repair uses dexterous tools and an endoscope that may improve precision. We have developed 3 mm diameter robotic tools that are optimized for cleft palate repair. This study investigates the performance of these tools using a high-fidelity cleft palate simulator test bed. METHODS: The 3 mm robotic instruments comprise of a wristed tool tip that couples to a da Vinci Research Kit (dVRK). The dVRK comprises two instrument arms with trocars and an endoscope for visualization. A virtual simulation was performed in MATLAB to determine the number of collisions (tool-tool and tool-oral cavity) based on the position along the palate and the height of the robotic arms (distance from trocar to oral aperture). Two experiments were performed using the 3 mm tools: (1) a MATLAB simulation validation involving suturing the cleft palate simulator oral mucosa at an anterior, middle and posterior position along the palate at three trocar heights and (2) a robotic assisted cleft palate repair on a high-fidelity cleft palate simulator. Both experiments were video recorded. The impact of tool position along the palate and the height of the robotic arms on collisions was determined for both experiments. RESULTS: The MATLAB simulation demonstrated increased collisions anteriorly and posteriorly with fewer collisions along the middle of the palate. The number of collisions increased with increasing trocar distance. The MATLAB simulation demonstrated a trend of increasing collisions anteriorly and posteriorly along the palate and increased collisions as the robotic arm trocar distance from the oral cavity increased (R2 = 0.9991, p = 0.0187). A robotic assisted cleft palate repair was successfully performed on the cleft palate simulator using the 3 mm tools. Lateral relaxing incisions, release of the velar muscles from the posterior hard palate shelf, suturing of the nasal mucosa, muscles and oral mucosa was successfully performed. Overall, collisions increased from anterior to posterior along the palate (mean increases of: 1.2 collisions/throw for nasal mucosal closure, R2 = 0.374, p = 0.005; 4.3 collisions/throw for oral mucosal closure, R2 = 0.407, p = 0.065) during the simulated cleft palate repair. CONCLUSION: A simulated robotic-assisted cleft palate repair was successfully performed using novel 3 mm diameter robotic tools. The tools were successful in executing the critical steps of a cleft palate repair. Instrument collisions were more frequent posteriorly and with increasing trocar height. Robotic assisted cleft palate repair using the 3 mm tools takes advantage of the robot’s superior visualization and dexterity and may improve the precision of cleft palate repair.

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)0.001
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 score0.055
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.024
GPT teacher head0.286
Teacher spread0.261 · 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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