Clinical and Microbiological Determinants of Ailing Dental Implants
Bibliographic record
Abstract
BACKGROUND: The failure of the host tissue to establish or maintain osseointegration around dental implants is due to either occlusal or parafunctional forces, premature loading, ill-directed stress, or microbial infection. The long-term failure rate of dental implants is generally 5-10%. Although a variety of etiologies of early peri-implant bone loss (from implant placement to 1-year post-loading) have been proposed, factors associated with late implant failures are less well understood but are probably related to both the peri-implant microbial environment and host factors. Discriminating between causes of implant failure is of importance for instituting a successful implant therapy. PURPOSE: The objective of this cross-sectional split-mouth study was to identify clinical, radiographic, and bacterial characteristics of peri-implant disease sites. MATERIALS AND METHODS: Fifteen patients with bilateral implants (Brånemark, Nobel Biocare AB, Göteborg, Sweden; and 3i implant systems, Implant Innovations Inc., Palm Beach Gardens, FL, USA) participated in the study. Sites with peri-implant (radiographic bone loss beyond the third implant thread) and peri-implant healthy tissues (radiographic bone level above the first implant thread) were identified in periapical radiographs using a long-cone paralleling projection technique. Microbiological identification was carried out using established anaerobic culture techniques. A descriptive statistics based on means and standard deviations was reported. RESULTS: Peri-implant bone loss was associated with the absence of radiographic crestal lamina dura, peri-implant pocket depth, pain on chewing, and the submucosal presence of the putative periodontopathogens Tannerella forsythia, Campylobacter species, and Peptostreptococcus micros. Pain was associated with P. micros, Fusobacterium species, and Eubacterium species. DISCUSSION AND CONCLUSION: The absence of radiographic crestal lamina dura and the presence of suspected major periodontal pathogens seem to be associated to peri-implantitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".