Autologous bone harvested during implant bed preparation: A randomized clinical trial comparing high‐speed drilling with irrigation versus low‐speed drilling without irrigation
Bibliographic record
Abstract
OBJECTIVE: To compare collected bone weight and the frequency of autologous bone harvesting from the flutes of surgical drills used for implant bed preparation using two different drilling techniques. A comparative evaluation of radiographic bone density and bone quality was also made. MATERIALS AND METHODS: A randomized clinical trial was made of 66 dental implants in patients with a single posterior edentulous site. The compared drilling techniques were high-speed drilling with irrigation (control group) and low-speed drilling without irrigation (test group). The bone collected in both groups was dried and weighed with a precise electronic balance. The frequency of harvesting was calculated. The median radiographic bone density of each implant site was measured using cone-beam computed tomography (CBCT) pixel values. Patient sex, age, implant position and dimensions, as well as the last drill diameter were analyzed as independent variables. The level of significance was 5%. RESULTS: The harvesting of bone chips from drill flutes proved possible in 51.5% of the procedures in the control group and in 100% in the test group (p < 0.001). There were also statistically significant differences in bone weight between the control group (6.7 ± 10.6 mg) and the test group (41.9 ± 30.3 mg) (p < 0.001). The CBCT pixel values were directly correlated with the collected bone weight in both groups. The median radiographic bone density, arch and last drill diameter were significantly associated with harvesting bone frequency and collected bone weight (p < 0.05). CONCLUSIONS: Our findings suggest that the frequency and weight of autologous bone harvested from drills are greater with low-speed drilling without irrigation than with high-speed drilling with irrigation. Radiographic bone density, arch and last drill diameter also significantly influenced the harvesting outcomes.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".