Plaque Accumulation Beneath Maxillary All‐on‐4™ Implant‐Supported Prostheses
Bibliographic record
Abstract
BACKGROUND: Maxillary prostheses supported by four implants, following the All-on-4(™) principles, have become an accepted effective treatment for totally edentulous patients. Maintaining the hygiene of such fixed implant-supported prostheses is challenging. PURPOSE: The purpose of this clinical study was to evaluate the distribution of plaque on the fitting surface of All-on-4 fixed prostheses in order to find new strategies for maintaining their hygiene. MATERIALS AND METHODS: Twenty All-on-4 maxillary fixed prostheses collected from 20 patients, 6 months after delivery, were stained with methylene blue to disclose plaque accumulation at the fitting surfaces of the prostheses. Digital photographs of the fitting surfaces of the prostheses were recorded and processed. The distribution of accumulated plaque was evaluated statistically. RESULTS: The average percentage of area covered with plaque was 28 ± 8% of the total area of the fitting surface of the prostheses. The fitting surfaces of the prostheses had three times more plaque on the palatal area (52.5 ± 7.33%) than on the buccal area (17.3 ± 7.33%, p < .05). The interimplant proximal areas of the fitting surface covered with plaque were high when the distance between implants was short (r = -0.326, p = .014). CONCLUSION: These findings suggest that the hygiene of the All-on-4 prostheses could be improved by maximizing the distances between the inserted implants in the jaw, minimizing the prostheses' palatal extension and guiding patients to optimize their oral hygiene practices targeting the palatal area of their prostheses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".