Analysis of the process parameters affecting the bone burring process: An in‐vitro porcine study
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".