The Effect of Maximum Bite Force, Implant Number, and Attachment Type on Marginal Bone Loss around Implants Supporting Mandibular Overdentures: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: There remains controversy regarding the clinical reasons for late-implant bone loss, which is a critical factor in the long-term success of implant-supported overdentures. PURPOSE: Assessment of the effect of such factors as attachment type, number of implants, gender, age, and maximum bite force (MBF) on marginal bone loss (MBL) around implants supporting mandibular overdentures. MATERIALS AND METHODS: Sixty-two edentulous patients rehabilitated with two-, three-, or four-implant-supported mandibular overdentures at a university clinic between January 2006 and January 2007 and having a digital panoramic radiograph at the time of loading, were included in this study. All patients received digital panoramic radiographs, and MBL was measured by subtracting bone levels from the first radiograph. MBF was measured using a bite force transducer. RESULTS: The amount of bone loss 48 months after loading was found to be unrelated to gender, age, implant number, attachment type, and splinting (p = .741, p = .953, p = .640, p = .763, p = .370, respectively). A significant correlation was observed between the MBF and the MBL of distal implants on the right side (p < .01, 79.9%) and the MBF and the MBL of distal implants on the left side (p = .011, 34.6%). CONCLUSIONS: MBL around implants supporting mandibular overdentures seems not to be affected by number of implants, attachment type, age, or gender; however, MBL is affected by MBF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".