Dental Implants in Patients with <scp>S</scp>jögren's Syndrome
Bibliographic record
Abstract
BACKGROUND: Limited evidence is available for applying dental implants in Sjögren's syndrome (SS) patients. PURPOSE: This study aims to retrospectively assess clinical outcome of implant therapy in a cohort of well-classified patients with SS. MATERIALS AND METHODS: All SS patients attending the University Medical Center Groningen for follow-up (n = 406) were asked whether they had implants. In SS patients with implants peri-implant health and implant survival was recorded and compared with data from matched healthy controls. Patients' symptoms, health-related quality of life, oral functioning, and satisfaction were assessed using validated questionnaires. RESULTS: Of the responding SS patients (n = 335), 21% was provided with implants. Of these 69 SS patients, 50 SS patients were willing to join our study. In SS patients, peri-implant health was reasonably good with minor marginal bone loss and a peri-implantitis prevalence of 14%, comparable with healthy controls. Implant survival was 97% (median follow-up 46 months) [IQR 26;73], and overall patients' satisfaction was high. Oral functioning correlated negatively with dryness, patients' satisfaction, and chewing ability in SS patients. CONCLUSIONS: Implant therapy is common in our cohort of SS patients. In spite of shortcomings of our retrospective analysis, implants in SS patients seem to perform comparable with implants in healthy patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".