Scanning electron microscopic analysis of adherent bacterial biofilms associated with peri‐implantitis
Bibliographic record
Abstract
OBJECTIVES: Peri-implantitis (PI) is caused by bacteria in the peri-implant space but the consensus on microbial profile is still lacking. Current microbial sampling of PI lesions has largely focused on analyzing bacterial species that have been shed from the implant surface and captured in the pocket fluid. The purpose of the present study was to investigate the morphotypes of bacteria in biofilm covering the implant threads and explore whether certain morphotypes were associated with PI. METHODS: Fourteen failed implants were removed and instantly processed for scanning electron microscope analysis. The implants were imaged at three equally divided sub-crestal levels of the exposed area. Bacterial morphotypes were identified and quantified by three examiners. Mobility and years in function were correlated to the presence of different morphotypes. RESULTS: The implants demonstrated the presence of variable bacterial morphotypes that did not correlate to disease progression in our study. Some implants were dominated by filaments and others showed the presence of combinations of cocci/rods or spirilles/spirochetes. In general, all implants showed variable morphologic biofilm composition. However, individual implants tended to have similar composition throughout the entire implant. Rods and filaments were dominant morphotypes throughout the surfaces and cocci showed increased presence toward the apical third. There were some differences in the biofilm morphology with mobility and time in function. CONCLUSIONS: The profiles of bacterial biofilm morphotypes in failing implants with similar clinical presentations were highly variable. While there were significant differences between implants, similar morphotypes in individual implants were often found throughout the entire surface.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".