A transcrestal sinus floor elevation strategy based on a haptic robot system: An in vitro study
Bibliographic record
Abstract
OBJECTIVES: To reveal the force profiles recorded by haptic autonomous robotic force feedback during the transcrestal sinus floor elevation (TSFE) process, providing a reference for the surgery strategy during TSFE. MATERIALS AND METHODS: A total of 42 maxillary sinus models with different angles of the sinus floor (30°, 40°, 50°, 60°, 70°, 80°, and 90°, compared to vertical plane) were 3D printed. Implant site preparation was performed using a robotic system, and the total force (Ft) and axial force along the drill (Fz) during the surgery were recorded by the haptic robotic arm. The actual initial breakthrough point (drill contacting sinus floor) and complete breakthrough point (drill penetrating the sinus floor) were defined visually (the actual IBP and the actual CBP). The theoretical initial breakthrough point (the theoretical IBP) and the theoretical complete breakthrough point (the theoretical CBP) defined by the robot-guided system and the CBCT were determined by real-time force feedback and imaging distance measurement, respectively. The distance from the bottom of the resin model to the actual IBP and the actual CBP was defined as Di and Dt, respectively. RESULTS: The difference in Fz began to increase significantly at 70°, while the difference in Ft became significant at 60°. When the angle was greater than 70°, there was no significant difference in the discrepancy between the actual and theoretical perforation points. Compared to judging the breakthrough point by CBCT, real-time force feedback TSFE under robotic surgery achieved more accurate initial breakthrough point detection. CONCLUSIONS: The smaller the angle, the larger the breakthrough force for the drill. The real-time force feedback of haptic robotic system during TSFE could provide reliable reference for dentists. More clinical studies are needed to further validate the application of robotic surgery assisted TSFE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".