The prosthetic abutment height can affect marginal bone loss around dental implants
Bibliographic record
Abstract
BACKGROUND: Marginal bone loss (MBL) is considered an important determinant of implant success, and establishing the peri-implant biological width has been regarded to influence MBL around implants. However, few studies have attempted to show the relationship between the crown/abutment gap and MBL. PURPOSE: To evaluate the effect of the prosthetic abutment height on MBL of dental implants. MATERIALS AND METHODS: This study evaluated data which were retrospectively collected through chart and panoramic radiographs of 145 patients (78 males and 67 females; aged 19 to 79 years, mean age 54.1 years) in whom 273 implants were placed by a single clinician between June 2009 and December 2014. The abutment height and the bone level were measured in digital panorama radiographs. All correlations between abutment height and MBL were analyzed using Spearman's test (P < .05). RESULTS: The 273 implants comprised 126 in 67 female patients and 147 in 78 male patients. The mean age of the patients was 54.1 years (range 19-79 years). The prevalence of MBL and the mean MBL decreased as the abutment height increased. Spearman's test showed a significant negative correlation between abutment height and MBL (P < .05). CONCLUSION: The present study suggests that implants with a higher prosthetic abutment show less MBL, with the abutment height recommended to not exceed 4 mm.
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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 | 0.004 |
| 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 | 0.000 |
| 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".