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Record W2904340297 · doi:10.1097/gox.0000000000001929

Preoperative Modeling for Mirror Hand: Simplifying a Difficult Problem Using 3-Dimensional Printing and Simulation

2018· article· en· W2904340297 on OpenAlexaff
Dale J. Podolsky, Gregory H. Borschel

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital limb and hand anomalies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsThree dimensional printingPlan (archaeology)Computer scienceDeformity3d modelCastingSurgical planning3d printerSurgery3D printingEngineering drawingMedicineComputer visionArtificial intelligenceEngineeringMechanical engineeringMaterials scienceGeology

Abstract

fetched live from OpenAlex

Mirror hand is an extremely rare congenital anomaly. We modeled and simulated the reconstruction of mirror hand in a 2-year-old boy utilizing a preoperative 3-dimensional model to aid surgical planning. A soft-tissue hand model was created using preoperative imaging, 3-dimensional printing, and silicone casting, and the model was used to perform trial surgery. Given the complexity of the deformity, the model greatly facilitated the final operative plan for reconstruction.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.310
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations3
Published2018
Admission routes1
Has abstractyes

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