Optimal parameters to avoid thermal necrosis during bone drilling: A finite element analysis
Bibliographic record
Abstract
The drilling bone may potentially cause excessive frictional heat, which can lead to local bone necrosis. This heat generation and local necrosis has been suggested to contribute to the resorption of bone around the placed screws, ending in loss of screw purchase in the bone and inadvertent loosening and/or the bone-implant construct. In vivo studies on this subject have inherent obstacles not the least of which is controlling the variables and real time bone temperature data acquisition. Theoretical models can be generated using computer software and the inclusion of known constants for the mechanical properties of metal and bone. These known Data points for the variables (drill bit and bone) enables finite element analysis of various bone drilling scenarios. An elastic-plastic three-dimensional (3D) acetabular bone mode was developed and finite element model analysis (FEA) was applied to various simulated drilling procedures. The FEA results clearly indicate that the depth of drilling and the drill speed both have a significant effect on the temperature during drilling procedures. The reduction of the feeding speed leads to a reduction in bone temperature. Our data suggests that reducing the feeding speed regardless of RPMs and pressure applied could be a simple useful and effective way to reduce drilling temperatures. This study is the first step in helping any surgeon who drills bone and places screws to better understand the ideal pressure to apply and drill speed to employ and advance rate to avoid osteonecrosis. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2386-2391, 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".