Influence of repeated implant‐abutment manipulation on the prevalence of peri‐implant diseases in complete arch restorations. A retrospective analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the effects of repeated abutment manipulation on the prevalence of peri-implant diseases. MATERIALS AND METHODS: A total of 27 edentulous patients (n = 108 implants) immediately restored with double-crown retained implant-supported prostheses were identified for this retrospective study. The test included the one-abutment, one-time care concept (n = 18 patients, n = 72 implants, OAOT) and the control abutment replacement (n = 9 patients, n = 36 implants, AR). A mixed effects model regression was conducted for the variable diagnosis (healthy, peri-implant mucositis, and peri-implantitis) with predictors abutment replacement (presence/absence), number of abutment replacement, category of keratinized mucosa (KM) (2 < KM ≥2 mm), and radiographic bone loss (BL). RESULTS: After 3-15 years (mean 10.2 ± 2.8 years), the prevalence of peri-implant mucositis and peri-implantitis in patients in the AR group was 11.1% and 88.9%, corresponding to 22.2% and 55.6% at the implant level, respectively. In OAOT group, none of the implants showed peri-implant mucositis, whereas the prevalence for peri-implantitis at patient and implant level amounted to 5.6% and 5.6%, respectively. The increased number of abutment replacements was significantly associated with the increased probability to diagnose peri-implant mucositis and peri-implantitis (OR: 6.13; 95% CI [2.61, 14.39]) (p < 0.001), whereas the presence of keratinized mucosa was not founded as a significant cofounder. The estimated mean BL in AR group was 1.38 mm larger than in OAOT group (p = 0.0190). CONCLUSIONS: The OAOT concept was associated with a lower prevalence of peri-implant diseases.
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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 | 0.000 |
| 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".