Correlation between Initial <scp>BIC</scp> and the Insertion Torque/Depth Integral Recorded with an Instantaneous Torque‐Measuring Implant Motor: An in vivo Study
Bibliographic record
Abstract
BACKGROUND: Quantitative intraoperative evaluation of bone quality at implant placement site and postinsertion implant primary stability assessment are two key parameters to perform implant-supported rehabilitation properly. A novel micromotor has been recently introduced allowing to measure bone density at implant placement site and to record implant insertion-related parameters, such as the instantaneous, average and peak insertion torque values, and the insertion torque/depth integral. PURPOSE: The aim of this study was to investigate in vivo if any correlation existed between initial bone-to-implant contact (BIC) and bone density and integral values recorded with the instrument. MATERIALS AND METHODS: Twenty-five patients seeking for implant-supported rehabilitation of edentulous areas were consecutively treated. Before implant placement, bone density at the insertion site was measured. For each patient, an undersized 3.3 × 8-mm implant was placed, recording the insertion torque/depth integral values. After 15 minutes, the undersized implant was retrieved with a 0.5 mm-thick layer of bone surrounding it. Standard implants were consequently placed. Retrieved implants were analyzed for initial BIC quantification after fixation, dehydration, acrylic resin embedment, sections cutting and grinding, and toluidine-blue and acid fuchsine staining. Correlation between initial BIC values, bone density at the insertion site, and the torque/depth integral values was investigated by linear regression analysis. RESULTS: A significant linear correlation was found to exist between initial BIC and (a) bone density at the insertion site (R = 0.96, explained variance R(2) = 0.92) and (b) torque/depth integral at placement (R = 0.81, explained variance R(2) = 0.66). CONCLUSIONS: The system provided quantitative, reliable data correlating significantly with immediate postinsertion initial BIC, and could therefore represent a valuable tool both for clinical research and for the oral implantologist in his/her daily clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".