Novel electrical conductivity device for osteotomy preparation for dental implants placement: A cadaver study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the accuracy, safety, and anticipation effect of a novel electrical conductivity device (SG) in maxillary osteotomy preparation for placement of dental implants. MATERIALS AND METHODS: Thirty-seven osteotomies were prepared by three operators with different levels of expertise, using the SG protocol in the maxilla of six fresh frozen cadavers. A pre-op CT measurement of the length of bone in the desired implant location was taken and compared with the final length of the osteotomy created using SG during surgery. A comparison was made between the results of the different operators. RESULTS: The pre-op CT bone length measurements and the final depth assessment of the osteotomy with SG had a very high correlation level (0.977) with a significant mean difference of 0.639 mm (P < .0001), with the pre-op CT measurements being longer. The least experienced operator had placed the implants 0.924 mm less deep than the pre-op CT length measurements while the most experienced operator had placed the implants 0.244 mm less deep than the pre op CT length measurements. All implants were placed in the correct position and no breach of the sinus/nasal floor or buccal/palatine bone plates was detected. CONCLUSIONS: The SG electrical conductivity device offers the operator real-time monitoring during the surgical procedure. It provides a simple, safe, and sensitive method of detecting breaches, making it simple and safe for oral surgeons with different levels of expertise to use, with promising results.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".