Prospective observational cohort study of the change of the marginal bone crest in relation to the prosthetic abutment height and the peri‐implant vertical mucosal thickness at implants positioned subcrestally
Bibliographic record
Abstract
AIM: To evaluate the influence on peri-implant crestal bone loss exerted by the vertical mucosal thickness and the abutment height over 12 months after placement of the restoration on subcrestal implants with change of platform, using a restoration abutment platform smaller than the implant platform. MATERIALS AND METHODS: A total of 99 implants were rehabilitated in the maxillary and mandibular posterior regions. A total of 22 implants were rehabilitated in the maxilla and 77 implants in the mandible, using digitally designed customized abutments with Atlantis weborder software, from the commercial house Dentsply Sirona (Dentsply Sirona S.A., Barcelona, Spain), version 4.6.5, adapting the height to the vertical thickness of the mucosa. Clinical and radiographic monitoring begins during the surgical procedure of placement of the implant and ends 12 months afterwards. Crestal bone loss was evaluated through the Carestream® CS8100 3D radiographic equipment. RESULTS: In all cases, the greatest loss of marginal bone occurred between the day of surgery (Tx) and placement of the rehabilitation (To). The average bone loss between both times was greater when the abutment height and vertical mucosal thickness did not exceed 3 mm. Subsequently, bone loss slowed and stabilized at 12 months. CONCLUSIONS: The minimum abutment height and the vertical mucosal thickness are factors to take into account when minimizing peri-implant marginal bone loss, the abutment height having the greatest importance according to the clinical data obtained.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".