The Diagnosis of Periodontal Disease in Private Practice
Bibliographic record
Abstract
BACKGROUND: Periodontal disease is an inflammatory reaction to the bacteria in dental plaque. The present study compared the prevalence of periodontal disease in patients using as a diagnostic either probing depth measurements, an inflammatory marker such as numbers of white blood cells in plaque samples, or microbiological markers such as the microscopic count and the benzoyl-DL-arginine naphthylamide (BANA) test. METHODS: Teeth with the most inflammation and/or deepest pockets in each quadrant were probed and subgingival plaque was sampled from 1,043 consecutive new patients enrolled in a private practice. Multivariate "diagnostic" models were developed based upon the probing depth (general linear models), percentage of white blood cell-positive and percentage of BANA-positive plaques (logistic regression models) to determine the prevalence of patients with periodontal disease. RESULTS: Plaque samples were removed from 3,694 sites. Fifty-two percent of sampled pockets were >4 mm; 49% of sites were inflamed, using the presence of white blood cells, and 28% were infected using the BANA test. Diagnostic models were highly significant at P<0.0001. The white blood cell model was the most parsimonious as demonstrated by the lowest Akaike information criteria statistic and had the highest receiver operator characteristic (ROC) curve relative to the probing depth and BANA models. CONCLUSIONS: Periodontal disease can be diagnosed chairside by the presence of white blood cells in plaque samples, a finding that reflects the inflammatory nature of the disease process. This approach would reduce the misclassification of subjects as having periodontal disease (130 patients in the present study who had pockets) but minimal evidence of an inflammatory response.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".