Reasons for Marginal Bone Loss around Oral Implants
Bibliographic record
Abstract
BACKGROUND: The reasons for long-term marginal bone loss around oral implants are not well understood. PURPOSE: The aim of this paper is to analyze presented evidence behind anticipated reasons for long-term marginal bone loss around oral implants. MATERIALS AND METHODS: A computerized research was conducted on PubMed in April 2011 with the following keywords: oral implants and marginal bone resorption/crestal bone loss/bone loss/bone resorption. This search resulted in a total of one thousand one hundred ninety-four papers of which seven hundred fifty-three were clinical contributions. Further search and filtering finally resulted in 21 experimental studies and one hundred sixteen clinical studies, which were reviewed. RESULTS: No evidence was found that primary infection caused marginal bone resorption. Clinical papers that have reported high levels of peri-implantitis were not supported by data given. Clinical evidence was presented that the so-called combined factors (implant hardware, clinical handling, and patient characteristics) may lead to marginal bone resorption. However, once tissue damage has been caused by combined factors, inflammation and/or infection may develop secondarily and then result in peri-implantitis that may need particular clinical treatment. CONCLUSIONS: As marginal bone loss primarily depends on numerous background factors, it seems logical that, for example, the use of poorly constructed implants placed and handled by untrained clinicians may result in high numbers of patients with secondary problems in form of peri-implantitis; having said this, control of combined factors may likewise lead to very good clinical results where peri-implantitis would represent a very rare disease indeed even at follow-up times of 10 years or more.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".