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Record W2965413615 · doi:10.1002/rcs.2028

Analysis of the process parameters affecting the bone burring process: An in‐vitro porcine study

2019· article· en· W2965413615 on OpenAlex
Jonathan Kusins, O. Remus Tutunea‐Fatan, George S. Athwal, Louis M. Ferreira

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsWestern University
FundersCalifornia HIV/AIDS Research Program
KeywordsVibrationMaterials scienceProcess (computing)Biomedical engineeringMedicineAcousticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: A stable bone burring process, which avoids thermal osteonecrosis and minimizes harmful vibrations, is important for certain orthopedic surgical procedures, and especially relevant to robot-operated bone burring systems. METHODS: An experimental characterization of the effects of several bone burring process parameters was performed. Burring parameters were evaluated by resultant bone temperature, tool vibration, and burring force. RESULTS: An optimal combination of bone burring parameters produced minimums in both bone temperature (<40°C) and tool vibration (<4 g-rms). A cylindrical burr, oriented normal to the specimen, resulted in significantly higher temperatures (50.8 ± 6.8°C) compared with a spherical burr (33.5 ± 4.3°C) (P = .008). Regardless of the parameters tested, burring forces were less than 10 N. CONCLUSIONS: The recommended configuration, which minimized both bone temperature and vibrations experimentally, was a 6-mm spherical burr at 15 000 rpm with a 2 mm/s feed rate.

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.

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)0.002
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 score0.056
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.337
Teacher spread0.310 · 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