The influence of crown‐to‐implant ratio on marginal bone levels around splinted short dental implants: A radiological and clincial short term analysis
Bibliographic record
Abstract
BACKGROUND: The amount of marginal bone resorption around dental implants is considered to have a significant impact on implant stability as well as implant survival rates. PURPOSE: The aim of this prospective study was to investigate the influence of prosthetic as well as patient specific factors on marginal bone loss around short dental implants. MATERIALS AND METHODS: Seventy-six implants, which supported splinted crowns were included for investigation. All implants were from the same type and had an intraosseous length of 6.5 mm and a diameter of 4.0 mm. Twenty implants were additionally splinted onto longer ones. Measurements of marginal bone loss were performed at a mean of 12.38 months after prosthetic loading and the mean follow-up for clinical evaluation was 20.52 months. RESULTS: Overall two implant failures were recorded, revealing a survival rate of 97.3%. Marginal bone resorption around 72 short implants measured 0.71 mm (SD: 0.74 mm) and was found to have a strong correlation with calculated Crown-to-Implant ratio (r = .71; P < .001). Age, gender, insertion torque, implant surface area, location, position, bone quality, and insertion torque did not influence peri-implant bone loss after one year of loading. CONCLUSION: Within the limitations of the study, it is suggested that Crown-to-Implant ratios should not exceed 1.7 to avoid increased early marginal bone loss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".