How do the dimensions of peri‐implant mucosa affect marginal bone loss in equicrestal and subcrestal position of implants? A 1‐year clinical trial
Bibliographic record
Abstract
INTRODUCTION: There is evidence that the apico-coronal implant position and the mucosal phenotype can affect the extent of peri-implant bone loss. This clinical trial analyzes the bone remodeling and marginal bone loss that occur around conical-connection implants placed equicrestally and subcrestally, assessing the effect of the peri-implant soft-tissue phenotype. METHODS: Fifty-one patients received 56 implants of distinct diameters (3.5 mm Ø n = 6; 4.3 mm Ø n = 41; 5 mm Ø n = 9) in the posterior part of the maxilla or mandible. The implants were placed equicrestally, 1 mm subcrestally and >1 mm subcrestally, depending on the initial supracrestal tissue height (STH). After 3 months of non-submerged healing, single metal-ceramic screw-retained implant-supported crowns were placed. Longitudinal measurements of STH, mucosal thickness and keratinized mucosa width (KMW) were made at the time of implant placement (T0), crown placement (T1), and after 3 (T2) and 6 months (T3) of prosthetic loading. At each of these points, a radiographic evaluation of bone remodeling and marginal bone loss was also performed. RESULTS: STH was significantly greater for implants placed >1 mm subcrestally than for those placed 1 mm subcrestally. After 12 months of follow-up, a very significant (p < 0.001) loss of KMW was observed, in addition to a marginal bone loss of 0.08 ± 0.1, 0.15 ± 0.2, and 0.14 ± 0.2 mm in the groups placed equicrestally, 1 mm subcrestally and >1 mm subcrestally, respectively. After the multiple linear regression, marginal bone loss was found to depend primarily on KMW (β = -0.43), while also being affected by STH (β = 0.32) and implant diameter (β = -0.28). CONCLUSIONS: Marginal bone loss may be influenced by the position with respect to the bone crest, as well as the KMW, STH, and implant diameter. However, more well-controlled studies are needed to verify these above-mentioned findings with different implant designs and connections.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".