Performance of a Commercial Immunoassay for Detection and Differentiation of Periodontal Marker Bacteria: Analysis of Immunochemical Performance With Clinical Samples
Bibliographic record
Abstract
BACKGROUND: We employed a commercial immunoassay for simultaneous detection and differentiation of marker bacteria Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, and Prevotella intermedia and reassessed the immunochemical performance of the assay. METHODS: We compared the analytical performance of the immunoassay in our study of clinical samples from 249 periodontal patients in 2 private periodontal practices with the previously reported analytical performance of the same immunoassay. We also compared immunoassay measurements of the marker bacteria in clinical samples with values obtained in other studies by direct culture of the same organisms. RESULTS: The assay produced 3 times more high-end readings than reported previously. We also reassessed and revised previously published calibration curves for the immunoassay. The immunoassay provided measurements of the marker bacteria in clinical samples from our patients that were comparable to and consistent with measurements of the same bacteria by direct culture in other studies. CONCLUSIONS: We ascribe the increased sensitivity of the immunoassay in our study to: 1) a more standardized and vigorous sample dispersion that improves release of particulate and soluble antigens from dental plaque biofilm, and 2) better visualization of the reaction product of the enzyme-linked immunoassay. High-technology assays, such as diagnostic immunoassays, have a significant potential for future development in dental diagnosis, because they simplify detection and measurement of biologically important markers such as specific bacteria in clinical samples. Commercial assays also have an important potential for standardization of clinical measurements of biological markers.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".