Subgingival microbiota of renal transplant recipients
Bibliographic record
Abstract
Renal transplant patients undergoing immunosuppressive therapy may experience periodontal side-effects such as gingival overgrowth. This study evaluated the subgingival microbiota of renal transplant recipients with or without periodontal tissue destruction who may have concurrent gingival enlargement. Subgingival paper point samples taken from the deepest probing sites of 38 subjects (one per patient) were examined using direct microscopy and culture techniques. A complex microflora comprising gram-positive and gram-negative cocci, rods and filaments, fusiforms, curved rods and spirochetes was observed using microscopy. Yeasts were occasionally detected. Significantly higher proportions of gram-positive morphotypes, including gram-positive cocci, were observed in samples from periodontally healthy patients. The predominant cultivable microflora from anaerobic culture comprised several species of facultative and obligate anaerobes. Colonization of the subgingival sites by 'foreign' microbes that are normally dermal, intestinal or vaginal flora was detected in up to 50% of the samples. High mean proportions of lost or unidentified species were also occasionally noted. The results showed that the subgingival biofilm of renal transplant recipients with chronic periodontitis comprised mainly gram-negative rods and spirochetes. Besides the usual predominant cultivable subgingival microbiota associated with periodontitis, the high prevalence of unidentified and 'foreign' microbes indicates the possibility of subgingival microbial alteration in renal transplant patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".