Some Biomechanical and Histologic Characteristics of Early‐Loaded Locking Pin and Expandable Implants: A Pilot Histologic Canine Study
Bibliographic record
Abstract
BACKGROUND: A two-stage approach with a 3- to 6-month healing period is recommended for the "conventional" osseointegration technique with oral implants. This may induce inconvenience and discomfort for patients, and immediate early loading protocols are preferable. PURPOSE: To compare a new type of implant with two locking pins, designed to allow immediate loading, with an expandable implant design with regard to bone tissue response and implant stability. MATERIALS AND METHODS: The molars and premolars of two beagle dogs were extracted in the mandible, and two types of implants (an apically expandable implant and a locking pin implant) were immediately placed in the sockets. The dogs received at least four implants (two of each type) in each side of the mandible. Implants were loaded with gold-palladium bridges 15 days later. The loaded implants were left for 3 months, and the dogs were sacrificed. Resonance frequency analysis (RFA) was performed at placement and sacrifice. Ground sections for histomorphometry were produced for each implant. RESULTS: Implant stability as measured by RFA was similar for the two types of implants before healing. At termination of the study, stability was higher for the locking pin implants. Bone histomorphometry showed that both types of implant were anchored by the same amount of bone and that bone-titanium interfaces did not differ. CONCLUSION: The locking pin implant showed better secondary stability than did the expandable implant, probably because of a better transmission of strains to bone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".