<i>In vivo</i>determination of multiple indices of periodontal inflammation by optical spectroscopy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Visible, near-infrared (optical) spectroscopy can be used to measure regional tissue hemodynamics and edema and therefore may represent an ideal tool with which to study periodontal inflammation in a noninvasive manner. The study objective was to evaluate the ability of optical spectroscopy to determine simultaneously multiple inflammatory indices (tissue oxygenation, total tissue hemoglobin, deoxyhemoglobin, oxygenated hemoglobin and tissue edema) in periodontal tissues in vivo. MATERIAL AND METHODS: Spectra were obtained, processed and evaluated from healthy, gingivitis and periodontitis sites (n = 133) using a portable optical, near-infrared spectrometer. A modified Beer-Lambert unmixing model that incorporates a nonparametric scattering loss function was used to determine the relative contribution of each inflammatory component to the overall spectrum. RESULTS: Optical spectroscopy was harnessed to generate complex inflammatory profiles of periodontal tissues. Tissue oxygenation at periodontitis sites was significantly decreased (p < 0.05) compared to sites with gingivitis and healthy controls. This was largely the result of an increase in deoxyhemoglobin in the periodontitis sites compared with healthy (p < 0.01) and gingivitis (p = 0.05) sites. Tissue water content per se showed no significant difference between the sites, but a water index associated with tissue electrolyte levels and temperature differed significantly between periodontitis sites and both healthy and gingivitis sites (p < 0.03). CONCLUSION: This study established that optical spectroscopy can simultaneously determine multiple inflammatory indices directly in the periodontal tissues in vivo. Visible, near-infrared spectroscopy has the potential to be developed into a simple, reagent-free, user-friendly, chairside, site-specific, diagnostic and prognostic test for periodontitis.
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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 | 0.000 | 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".