MétaCan
Menu
Back to cohort
Record W4415439337 · doi:10.1302/1358-992x.2025.10.058

PATIENT-SPECIFIC 3D-PRINTED DRILL GUIDES FOR THE PLACEMENT OF GRAFTS IN AN OVINE OSTEOCHONDRAL ALLOGRAFT TRANSPLANTATION MODEL

2025· article· en· W4415439337 on OpenAlexaff
R. Peter Suderman, Mark Hurtig, S. Aloi, Marc D. Grynpas, Paul R.T. Kuzyk, Adele Changoor

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCadaveric spasmDrillTransplantationFemur3d modelJoint (building)3d printedSoft tissue

Abstract

fetched live from OpenAlex

Osteochondral allograft transplantation is a surgical repair technique that uses cadaveric donor tissue to treat cartilage lesions. In humans, anatomical location matching is used to identify the location of donor graft harvest, with the goal to transplant a graft that sits flush with surrounding cartilage and recreates joint curvature. The ovine pre-clinical model is commonly used for osteochondral repair research due to anatomical and weight-bearing similarities, but differences, including thin cartilage, smaller joint surfaces and limited lateral access due to the long-digital extensor tendon in sheep remain limitations. In ovine allograft models the use of small cylindrical grafts and graft placement variability resulting from the free-hand drilling technique present challenges for correctly matching donor and recipient tissues, and may increase variability that impedes study outcome interpretation. We have designed and constructed 3D-printed, patient-specific drill guides for ovine osteochondral allograft transplantation. Collaboration within the multidisciplinary group helped determine the design requirements to ensure guides would meet standards related to usability, strength, sterilizability and soft tissue interference. Ovine tibiofemoral joint CT scans were segmented for bone using open-source software (Slicer 3D 5.0.2) to create a recipient distal femur 3D model. Custom software [1], was used to plan graft placement for three sites in the recipient joint by creating 3D models of guide cylinders. Guides were then designed in 3D modelling software (Materialise Magics v.26) using the recipient distal femur and cylinder models. Guides were printed using an Ultimaker 3 Extended with eSun Polylactic Acid+. An iterative design procedure was conducted, with each iteration tested against design requirements. Once design requirements were satisfied, the final design (Figure 1) was used to create patient-specific drill guides for ovine osteochondral allograft transplantation performed on 14 sheep. Twenty-three female Rideau-Arcott sheep were used (Four – six years, 75.8 ± 13.8 kg). Nine sheep were sacrificed to harvest 18 distal femurs for collection of donor grafts. Fourteen sheep underwent bilateral osteochondral allograft transplantation, resulting in 28 operated joints. During each procedure, three donor grafts, each 6.3 mm in diameter, were transplanted into locations on the medial trochlea, lateral trochlea and medial femoral condyle. Feedback on guide use was positive with 70 of 84 grafts visually well-matched with surrounding tissue. Eight of 84 guides were noted to interfere with soft tissue, primarily the patellar tendon, but only half of these (four of 84) unable to be used due to interference. 3D-printed drill guides were successfully developed and implemented in an ovine model of osteochondral allograft transplantation. The guides supported perpendicular placement of grafts and matching of joint surface curvature at multiple sites. The use of patient-specific drill guides has usefulness in specialized research settings where multiple grafts are used and proper placement of grafts is a necessity. For any figures or tables, please contact the authors directly.

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)0.000
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 score0.237
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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

Explore more

Same venueOrthopaedic ProceedingsSame topicDental Implant Techniques and OutcomesFrench-language works237,207