A New Approach for Creating Defined Geometries by Navigated Laser Ablation Based on Volumetric 3-D Data
Bibliographic record
Abstract
This paper describes a new approach for laser bone treatment according to a preoperative plan. The advantages of using laser systems are the free choice of the cutting geometry and the possibility of bone treatment without any severe thermal damage. On the other hand, the control of bone removal depth is difficult. Due to the lack of haptical feedback, it is only possible to control the bone removal visually. In addition, by selecting wrong laser parameters and incorrect handling,the tissue can sustain thermal damage. To solve this problem, an approach of navigated and model-based calculation of depth ablation has been investigated. The focus of this paper was to verify the feasibility of precise and safe laser bone removal by combining navigation information with mathematical and volumetric modeling. For the mathematical modeling, known approaches are used. On the basis of CT data, cavities in a bovine bone were planned with a navigation system. With an optical measurement system, the position of the laser handpiece was calculated relative to the bone. Using a mathematical model, the theoretical cavity depth was calculated for each laser pulse and displayed on the navigation screen. Thereby, the material removal was determined in a volume model. With this information, five cavities were created by the laser using constant energy settings. A final measurement of the cavities' depths showed an error of less than 1 mm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".