Impact of High Insertion Torque on Implant Surface Integrity
Bibliographic record
Abstract
INTRODUCTION: The long-term success of dental implants depends on the preservation of supporting tissues over time. Recent studies have highlighted the release of titanium particles as a potential etiology for the onset and progression of peri-implant diseases modulated by inflammatory biomarkers. This study provides a comprehensive analysis of surface changes associated with high insertion torque placement. METHODS: Three groups of cylindrical threaded dental implants, each representing different surface topographies produced by anodization or a combination of grit-blasting and acid-etching processes, were inserted into fresh cow rib bone blocks used to mimic human jaws. Individual bone blocks were fabricated with a dimension of 20 × 15 × 15 mm, randomly assigned to the three implant groups. Prior to dental implant placement, the bone blocks were divided in half to facilitate implant removal without introducing additional damage. The drilling protocol was modified, excluding the final drill recommended by the manufacturer to ensure higher insertion torque values during the procedure. Dental implants were removed from the bone blocks and processed for analysis. Surface roughness was characterized using interferometry on the same area before and after insertion. Scanning electron microscopy (SEM) with a back-scattered electron detector (BSD) was employed to identify the implant surface and loose particles at the bone block interface. RESULTS: The high insertion torque protocol used in this study resulted in higher insertion torque values compared to manufacturers' protocol, but no difference was observed when comparing the three implant groups. Surface roughness characterization revealed that amplitude and hybrid roughness parameters for all three groups were lower after insertion. The surfaces exhibiting a predominance of peaks (Ssk [skewness] > 0) associated with higher structures (height parameters) showed greater damage at the crests of the threads, while no changes were observed in the valleys of the threads. SEM-BSD images revealed loose titanium particles at the bone blocks interface, predominantly at the crestal cortical bone level. CONCLUSIONS: High insertion torque resulted in surface damage at the crests of threads, which subsequently led to the release of titanium particles primarily at the bone crest. The initial release of titanium particles during implant insertion at the bone-implant interface warrants further exploration as a potential cofactor for marginal bone loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".