Etiology and classification of food impaction around implants and implant‐retained prosthesis
Bibliographic record
Abstract
BACKGROUND: Food impaction is a common risk factor for the initiation of peri-implant inflammation and failure of the osseointegrated implant. Although clinicians do acknowledge the presence of food impaction around implants and implant-retained prosthesis, no classification system has yet classified the food impaction around the implant and implant-retained prosthesis. PURPOSE: The present paper aims to identify and classify the plausible etiology of food impaction around implants and implant-related prosthesis. MATERIALS AND METHODS: The following search terms were utilized for data search: "Food Impaction" AND "Implants" AND "Food Impaction" AND "Perimplantitis" AND "Food Impaction" AND "Classification." Articles that were written in the English language in PubMed and Cochrane Library database from 1930 till September 2018 were scrutinized. A total of 24 articles were scrutinized, out of which only 15 articles were selected. RESULTS: Food impaction around implants is broadly classified into five categories based on the number of implants, nature of implants prosthesis involved for replacement and relation of the implant prosthesis to the adjacent tooth, restoration, or prosthesis. CONCLUSION: This is the first classification designed to classify food impaction around dental implants and implant-retained prosthesis. The classification can be used by clinicians for optimal diagnosis, interpretation, and treatment plan for 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".