MétaCan
Menu
Back to cohort
Record W4210920722 · doi:10.1097/bto.0000000000000581

“In-house” Design and Use of 3-dimensional Printed Patient-specific Bone Tumor Resection Guides for Geometric Osteotomies in Sarcoma Surgery

2022· article· en· W4210920722 on OpenAlexaff
Joseph K. Kendal, Murray T. Wong, Spencer J. Montgomery, Brent Benavides, Michael J. Monument, Shannon Puloski

Bibliographic record

VenueTechniques in Orthopaedics · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of TorontoAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineOrthopedic surgerySurgerySarcomaOsteotomyResectionChondrosarcomaHemipelvectomySurgical teamRadiologyPathology

Abstract

fetched live from OpenAlex

Three-dimensional printing technology has rapidly advanced as a promising technology for preoperative planning, education, and surgical execution in orthopedic surgery. Use of patient-specific instrumentation in orthopedic oncology sarcoma cases can streamline complex osteotomies while providing safe margins based on predetermined osteotomy levels. We describe use of an “in-house” protocol to create patient-specific bone tumor resection guides for use in orthopedic oncology cases. The described protocol bypasses expensive outsourcing options and facilitates use of preoperative surgical simulation and intimate involvement of the surgical team in the guide design. We report on the successful design and use of three-dimensional printed patient-specific bone tissue resection guides in a case of proximal tibial parosteal osteosarcoma resection and reconstruction with a size-matched allograft, and in a case of a secondary pelvic chondrosarcoma resection.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.027
GPT teacher head0.244
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 designOther design
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTechniques in OrthopaedicsSame topicAnatomy and Medical TechnologyFrench-language works237,207