Assessment of the hemodynamic profile in periodontal tissues of diabetic subjects with periodontitis by optical spectroscopy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The influence of diabetes mellitus (DM) on the hemodynamics of periodontal tissues has not been assessed previously. The primary objective of this study was to validate optical spectroscopy as a periodontal diagnostic tool for subjects with type 2 DM and chronic periodontitis. MATERIAL AND METHODS: Using a portable optical near-infrared spectrometer, optical spectra were obtained from healthy (n = 127), gingivitis (n = 115), and periodontitis (n = 109) sites of 65 subjects with type 2 DM and chronic periodontitis. Healthy (n = 65) sites of 15 nondiabetic subjects without periodontitis were used as controls. A modified Beer-Lambert unmixing model that incorporates a nonparametric scattering-loss function was used to determine the relative contribution of deoxygenated hemoglobin and oxygenated hemoglobin (HbO2 ) to the overall spectrum. The balance between tissue oxygen delivery and oxygen utilization in periodontal tissues was assessed. RESULTS: In diabetic subjects, tissue oxygen saturation and HbO2 concentration were significantly decreased in the periodontitis sites (p < 0.01) compared with the healthy and gingivitis sites. Furthermore, tissue oxygenation in healthy sites of control subjects was significantly higher than that in sites of diabetic subjects (p < 0.01). CONCLUSION: In summary, the results of this study suggest that optical spectroscopy can monitor the hemodynamic profile in diabetic subjects with chronic periodontitis. Furthermore, healthy sites of diabetic subjects presented lower tissue oxygenation than did those of nondiabetic subjects.
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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".