Marginal bone loss around dental implants: comparison between matched groups of bruxer and non‐bruxer patients: A retrospective case–control study
Bibliographic record
Abstract
PURPOSE: To compare marginal bone loss (MBL) around dental implants in a group of bruxers in relation to a matched group of non-bruxers. METHODS: The present record-based retrospective study included patients selected from individuals treated with dental implants at one specialist clinic in Malmö. Only implants not lost and with baseline radiographs taken within 12 months after implant placement and with a minimum of 36 months of radiological follow-up were considered for inclusion. Univariate linear regression models and a linear mixed-effects model were performed. RESULTS: Two hundred and four patients (104 bruxers, 100 non-bruxers), with a total of 811 implants (416 in bruxers, 395 in non-bruxers) were included in the study. The results of the linear mixed-effects model suggested that bruxism, smoking, age, region of the jaws, implant diameter, and prosthesis type had a statistically significant influence on MBL over time. Individuals who are both bruxers and smokers showed greater MBL when compared to individuals who are either a bruxer or smoker, or neither (p < 0.001). CONCLUSIONS: Bruxism is suggested to increase the risk of MBL over time, as well as higher age, smoking, and the combination of bruxism and smoking. Other factors that showed a correlation with increased MBL were implant diameter, region of the jaws, and prosthesis type, but it is not possible to draw robust conclusions for these factors, as the categories of these variables were very unbalanced.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".