Radiographic Analyses of “Advanced” Marginal Bone Loss Around Brånemark® Dental Implants
Bibliographic record
Abstract
BACKGROUND: Dental implant failures have a multifactorial background; dependency within patient/jaw exists. Failures caused by bone loss are rare. Lately, advanced bone loss around implants has been discussed. PURPOSES: Our aim was to study advanced bone level changes (>or=2 mm) regarding "clustering effect," prediction, and dependency. Further, we also aimed to study if the number of radiographs/radiographic examinations could be reduced. MATERIALS AND METHODS: Six hundred and forty patients (3,462 Brånemark implants) with radiographic follow-ups >or=5 years were included, whereas patients with overdentures and augmentation procedures were excluded. RESULTS: Progression rate for implants with advanced bone loss was largest during the first year; thereafter, slow. A cluster effect was found with more advanced bone loss in few patients. Position was important for lower jaw implants with larger bone loss for implants placed close to midline. Age, jaw type, and implant placement were identified as predictors. The longer the follow-ups, the more bone loss around a randomly selected and examined implant, and the more implants per patient, the higher the risk for bone loss >or=2 mm around any other implant. Still, it seems safe to exclude radiographic follow-ups during the first 5 years. Dependency within the patient was found, hence the "one-implant-per-patient technique" can be applied. CONCLUSION: The number of intraoral radiographs per examination and, more importantly, radiographic examinations can be reduced without jeopardizing good clinical management, a statement valid even for Brånemark implants with advanced bone loss.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".