Brazilian oral medicine and oral pathology: international scientific collaborations
Bibliographic record
Abstract
Backgroun: Peri-implant diseases are common complications that may lead to dental implant failure. An adequate prosthesis design is crucial to reduce the risk of complications, and to improve peri-implant health. The present study was carried out to assess the effect of prosthesis design upon the presence of peri-implant inflammation. Material and methods: A cross-sectional study was conducted in patients with a single-unit implant-supported screw-retained crown. After removing the crowns, standardized photographs were made to assess several variables such as the length of the submucosal extension (SE) or the emergence angle (EA). Clinical signs of inflammation were also registered, and an experienced clinician probed the implants. The White (WES) and Pink Esthetic Scores (PES) were also recorded. Patients were classified into two groups according to the presence (positive bleeding on probing (BoP+)) or absence (negative bleeding on probing (BoP-)) of inflammation around the dental implant. Independent t-tests and one-way ANOVA were used to analyze the data. Results: A total of 90 implants were analyzed. Fifty-two implants (57.8%) had BoP+ while 38 (42.2%) had no signs of inflammation of the peri-implant tissues (BoP-). Long SE was significantly associated with BoP+ sites. The EA did not seem to be related to the presence of inflammation (p=0.642). PES/WES showed a negative correlation with buccal EA (r=-0.227; p=0.032). Conclusions: Long submucosal extensions in single-unit implant-supported crowns seem to be associated with peri-implant tissues inflammation (BoP+). A higher emergence angle on the buccal aspect was associated with poor esthetic outcomes.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.012 |
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".