Assessment of Local Hemodynamics in Periodontal Inflammation Using Optical Spectroscopy
Bibliographic record
Abstract
BACKGROUND: Among the newly emerging diagnostic approaches for periodontitis, optical spectroscopy is a promising complementary diagnostic tool. The objective of this study is to verify the reproducibility of this method at a geographically distinct location (Suzhou, China) to a broader patient population using similar instrumentation to that in a previous report. METHODS: Using a portable optical near-infrared spectrometer, optical spectra were obtained, processed, and evaluated from healthy (n = 62), gingivitis (n = 98), and periodontitis (n = 47) sites from a total of 51 patients. A modified Beer-Lambert unmixing model that incorporates a non-parametric scattering loss function was used to determine the relative contribution of oxyhemoglobin and deoxyhemoglobin to the overall spectrum. The balance between tissue oxygen delivery and oxygen use in periodontal tissues was then assessed. RESULTS: Tissue oxygenation decreased significantly from healthy sites to sites with gingivitis (P <0.01) and between gingivitis and periodontitis (P = 0.015). This is largely caused by a significant increase in deoxyhemoglobin between normal and gingivitis (P <0.01) and a concomitant decrease in oxyhemoglobin between gingivitis and periodontitis (P = 0.02). CONCLUSION: This study supports previous findings that tissue oxygenation as measured by optical spectroscopy is significantly decreased in periodontitis and that optical spectroscopy can simultaneously determine multiple inflammatory indices related to periodontal disease directly in gingival tissues in vivo.
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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 | 0.001 | 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".