Longitudinal analysis on the effect of insertion torque on delayed single implants: A 3‐year randomized clinical study
Bibliographic record
Abstract
BACKGROUND: Implant stability is commonly related to insertion torque. Recently, it has been suggested that higher insertion torque may lead to greater bone resorption. PURPOSE: The aim of the present randomized clinical study was to evaluate the role of different insertion torque values in terms of implant success, marginal bone loss, and facial soft tissues recession. MATERIALS AND METHODS: Patients requiring a single dental implant were recruited and randomized to receive one of two implants with the same macro-geometry but different cutting designs. First group consists of a 90 degrees cutting groove know as self-tapping implant, and the second group known as Blossom™ cutting groove. (Intra-Lock, Boca Raton, Florida). The insertion torque (IT) was assessed and two groups followed: high-IT (≥50 Ncm) group and regular-IT (<50 Ncm) group. After 3 months, all the implants were restored. At baseline, buccal bone thickness (BBT) was recorded. During the 3-year survey, the following outcomes had been registered: implant failures and success, radiographic marginal bone level around dental implant (MBL) and facial soft tissue level (FSTL). RESULTS: A hundred and sixteen implants were placed in healed sites. The overall survival rate after 3 years was 96.5%. The Cumulative Success Rate was 91.3% for the High IT group and 98.2% for the Regular IT group. The mean marginal bone loss and facial soft tissue recession, at a 3-year evaluation, were significantly greater for the High-IT group and in the mandible than that reached in the Regular-IT group and in the maxilla. CONCLUSION: Present findings showed that implants placed with higher insertion torque in mandible led to greater bone resorption and mucosal recession than that registered for implants placed with a regular IT. Moreover, sites with a thick buccal bone wall (≥1 mm) showed smaller recession at the facial soft tissue level after 3 years.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".