Effect of Age on Single Implant Submersion Rate in the Central Maxillary Incisor Region: A Long‐Term Retrospective Study
Bibliographic record
Abstract
BACKGROUND: It is contraindicated to place dental implants before growth and development are completed as they are at a risk of submersion due to growth arrest, creating a potential aesthetic problem. PURPOSE: The present study evaluated the effect of age on mean submersion rate of single dental implant in the central maxillary incisor area as compared with the adjacent natural tooth in implants placed after growth has ceased. MATERIALS AND METHODS: A retrospective study was conducted on 35 patients (mean age 29.3 ± 9.9 years, 21 females) who received a single dental implant replacing a missing maxillary central incisor from 1992 to 2008 with a follow-up of at least 3 years. Clinical photos from last follow-up were digitally analyzed to measure the vertical change between the incisal edge of the implant supported crown and the adjacent natural central incisor. RESULTS: In the younger age group (≤30 years), the submersion rate was more than three times higher than in the older age group (>30 years), yielding submersion rates of 1.02 and 0.27% per year, respectively. CONCLUSIONS: Whereas implant submersion continues throughout adult life, its rate varies with age. It is evident that this phenomenon is much more conspicuous during the second and third decades of life as compared with the fourth and fifth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".