Comparison of One-Drill Protocol to Sequential Drilling In Vitro and In Vivo
Bibliographic record
Abstract
This study compares the heat generated during bone drilling using different protocols and implant systems, first in vitro and then in vivo with an animal model. In the experimental phase, thermal data were collected using an infrared camera while preparing implant beds in bone similes. The heat generated by a one-drill protocol with a new-generation drill bit and the Straumann BLT sequential drilling protocol was evaluated. The experimental study was then replicated in an animal model to assess the impact of these protocols on early osseointegration, measured by bone-to-implant contact (BIC) at three weeks post-surgery for Straumann BLT SLActive and Medentika Quattrocone implants. The results showed the BLT sequential protocol generated significantly more heat during drilling in bone similes compared to the new-generation drill bit. In the animal model, a histological analysis revealed a trend favoring shorter drilling protocols, with reduced drilling times and a potential advantage for osseointegration, though the BIC differences were not statistically significant. These findings suggest that minimizing the number of drilling steps and thermal stress may enhance osseointegration more effectively than advanced implant surface treatments. This aligns with emerging views on the importance of optimized drilling protocols and designs to reduce heat generation and better preserve surrounding bone structure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".