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Record W4211020694 · doi:10.32920/ryerson.14657991.v1

Design of a prototype bioresorbable tibial implant in a sheep model

2021· preprint· en· W4211020694 on OpenAlexaff
Adrian Dudi Janura

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFinite element methodImplantBiomedical engineeringComputer scienceMaterials scienceOrthodonticsStructural engineeringEngineeringSurgeryMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was the design of a prototype calcium polyphosphate tibial implant for implantation into a sheep. In the design, several design parameters were considered: CPP implant structural strength, the maximum allowed micromotion of the structure at the interface with bone and the possibility of the surgeon implementing the necessary geometric changes on the bone elements during implant surgical insertion. A fininte element analysis facilitated the design and allowed for the effects of the various geometric parameters investigated. This analysis was based on a real solid model of the sheep tibial bone based on the CT scans of a five year old sheep, and several were the geometeric parameters investigated. The approach of the finite element analysis was very conservative, in the meaning that the load applied was purposely increased in order to account for any possibly unknown factors that may exist and not implemented in the analysis.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.287
Teacher spread0.242 · 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 designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

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