Displacement of Dental Implants in Trabecular Bone under a Static Lateral Load in Fresh Bovine Bone
Bibliographic record
Abstract
AIM: The study aims to provide objective data for the displacement of titanium screw implants in trabecular bone specimens. One hundred Semados implants (Bego, Bremen, Germany) were inserted in bovine type IV bone specimens. All implants had a diameter of 3.75 mm; 50 implants had a length of 8.5 mm and 50 implants had a length of 15 mm. Insertion torque was determined at intervals of 10, 20, and 30 Ncm. Implants were loaded horizontally with 10, 20, and 30 N for 2 seconds. An indicator strip was attached to the implant abutment to allow direct observation of implant movement relative to the bone surface. Horizontal displacement was assessed with an accuracy of measurement of 10 µm. Seven implants got lost by visible loosening. Degree of displacement was subject to evaluation with all others. Those implants showed a mean displacement of 59 µm for 10 N (n = 100), 173 µm for 20 N (n = 99), and 211 µm for 30 N (n = 93). The mean displacement of 15-mm implants (16, 37, 51 µm) was significantly lower compared with 8.5-mm implants (103, 311, 396 µm) corresponding to 10, 20, and 30 N as lateral loads. CONCLUSIONS: Displacement of screw implants in trabecular bone can be detected and visualized using commercially available endoscopes with a high magnification. A lateral load of 20 N indicates a mean displacement of over 100 µm and therefore results in a critical displacement.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".