Bone Response to Implant‐Supported Frameworks with Differing Degrees of Misfit Preload: In Vivo Study in Rabbits
Bibliographic record
Abstract
PURPOSE: To study the bone response around implants placed in tibia of rabbits that supported misfitting superstructures secured with different degrees of preload. MATERIALS AND METHODS: Twelve rabbits were provided with two terminal 10-mm and one intermediate 7-mm-long implant in each tibia. After an integration time of about 9 weeks, nine of the animals received one control framework each (n = 9), designed with good fit to all three implants. In the other tibia of these animals, and in both tibias in the remaining three rabbits, test frameworks (n = 15) were connected with a vertical misfit of about 1 mm to the intermediate implant. The intermediate set screws were tightened with a torque ranging from 15 Ncm to 26 Ncm in the different test frameworks. The fascia and skin was then sutured back over the implants. After a loading period of 2 to 3 weeks, the animals were sacrificed, and histomorphometric measurements were made and correlated to the different levels of preload of the central implant. RESULTS: The mean bone-to-metal contact for the three best consecutive threads of the central implant was 40% for both test and control sites (p > .05). Compared to the other regions of the implant thread, less bone-to-metal contact was found at the tip of the test implant threads in the low preload group (p < .05). However, the same relation was not observed in the high preload group. A significant correlation was observed between increasing degree of preload in the central screw joint and increasing bone-to-metal contact, most obviously noticed at the tip of the implant thread (p < .01). CONCLUSIONS: Misfit stress levels of clinical magnitudes do not seem to jeopardize osseointegration per se. On the contrary, clinical levels of preload stress seem to significantly promote bone remodeling at the tip of the implant thread.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".