Profile of bacteria colonizing the exposed bone of patients with anti-osteoclastic drug-related osteonecrosis of the jaws
Bibliographic record
Abstract
Microbial etiology for anti-osteoclastic drug-related osteonecrosis of the jaw (ARONJ) was suggested. This study investigates any link between bacteria colonizing ARONJ sites and other oral cavity sites. Microbiota samples of 10 ARONJ patients were collected from the exposed bone, adjacent teeth, contralateral teeth, and tongue. DNA checkerboard hybridization was used for microbiota analysis with 43 genomic DNA probes prepared from human oral bacterial (38) and candida (5) species, using Socransky's bacterial complexes as a guide. The frequency and the mean proportion of each bacterial species were used. Eikenella corrodens, Streptococcus constellatus, and Fusobacterium nucleatum were dominant in the ARONJ sites and detected in most teeth samples. Staphylococcus aureus was also dominant in the ARONJ sites and tongue. Significant correlations were found between the mean proportions of bacterial species colonizing adjacent teeth, contralateral teeth, and tongue (p < 0.001, R(2) > 0.69). No significant correlation (p > 0.05, R(2) < 0.025) was found between bacteria colonizing ARONJ sites and other evaluated sites. Within the study limitations, it was concluded that the primary sources of microorganisms colonizing ARONJ sites could be other sites such as teeth and tongue. The microbial profile of the necrotic bone is predominantly colonized with bacteria from Socransky's green and orange complexes, as well as with species associated with bone infections.
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 | 0.000 | 0.001 |
| 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".