Assessment of tissue oxygenation of periodontal inflammation in smokers using optical spectroscopy
Bibliographic record
Abstract
BACKGROUND: We have recently developed a periodontal diagnostic tool that was validated in non-smokers with periodontitis. Tobacco smoking is a recognized risk factor for periodontal diseases that can mask gingival bleeding and lead to a false negative diagnosis. Therefore, the purpose of current study is to further validate this instrument in smokers with periodontal diseases. METHODS: Using a portable optical near-infrared spectrometer, optical spectra were obtained, processed and evaluated from healthy (n = 108), gingivitis (n = 100), and periodontitis (n = 79) sites of 54 systemically healthy smokers. A modified Beer-Lambert unmixing model that incorporates a non-parametric scattering loss function was used to determine the relative contribution of deoxygenated haemoglobin (Hb) and oxygenated haemoglobin (HbO2 ) to the overall spectrum. The balance between tissue oxygen delivery and utilization in periodontal tissues was then assessed. RESULTS: Tissue oxygen saturation was significantly decreased in the gingivitis (p = 0.016) and periodontitis (p = 0.007) sites, compared to the healthy sites. There was a trend towards increased concentration of Hb and decreased concentration of HbO2 from healthy to diseased sites, without statistical significance (p > 0.05). CONCLUSIONS: Optical spectroscopy can determine tissue oxygenation profiles of healthy and diseased sites in smokers. The spectral profile of periodontal sites in smokers generally resembles those from non-smoking patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".