Reasons for Marginal Bone Loss around Oral Implants
Why this work is in the frame
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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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it