Clinical Outcome of Dental Implants Placed with High Insertion Torques (Up to 176 Ncm)
Bibliographic record
Abstract
BACKGROUND: Primary stability can be improved by using a tapered implant in a slightly underprepared implant site. This may lead to high compression forces and elevated insertion torques. It has been postulated that disturbance of the local microcirculation may occur, leading to necrosis of the osteocytes and bone resorption. PURPOSE: Report on the clinical outcome of 42 implants placed with an insertion torque equal or greater than 70 Ncm and evaluate bone levels around these implants. MATERIALS AND METHODS: This prospective study included 48 patients treated with 66 4.5 mm diameter Tapered Screw-Vent implants (Zimmer Dental®, Carlsbad, CA, USA). Maximum insertion torque (MIT) was recorded with an electronic torque measuring device (Tohnichi® STC200CN, Hitachi, Tokyo, Japan). Nine implants (control group) presented MIT between 30 and 50 Ncm (mean = 37.1 Ncm) and 42 implants (experimental group) MIT greater than 70 Ncm (mean = 110.6 Ncm, range: 70.8-176 Ncm). Marginal bone levels were recorded at the time of loading and 1 year later for the two groups. RESULTS: After 2-3 months of non-sumerged healing, all implants were clinically stable. Mean marginal bone resorption was 1.03 mm (SD = 0.44) for the control group (low torque) and 0.72 mm (SD = 0.56) for the experimental group (high torque) at time of loading, and 1.09 (SD = 0.62) and 1.24 mm (SD = 0.75), respectively, after 1 year. There were no significant differences between the two groups for bone stability and implant success rate. CONCLUSIONS: The use of high insertion torques (up to 176 Ncm) did not prevent osseointegration. Marginal bone levels in the control and experimental groups were similar both at the time of loading and 1 year later.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".