Periodontal care may improve systemic inflammation
Bibliographic record
Abstract
BACKGROUND: Periodontitis is an infectious chronic insidious disease of the tooth supporting structures that causes a general inflammatory response. The aims of the study were to determine whether periodontitis is associated with markers of general inflammation high-sensitivity (hs) C-reactive protein (CRP) leading to cardiovascular disease, and whether proper management of the periodontal disease would improve inflammation and thus, may prevent cardiovascular disease in the future. METHODS: This was a prospective case-controlled pilot study. Nine patients (3 women, 6 men; 40+/-5 yr) took part. All had severe periodontitis, without systemic disorders, and were all treated conservatively without surgical intervention. All had a 2nd visit after 3 months of treatment at the Outpatient Dental Clinic of the Hospital. Periodontal status and hs-CRP were evaluated on entry to the study and 3 months after treatment. Nine age and sex-matched healthy volunteers without periodontal disease served as the control group. RESULTS: Periodontal clinical parameters were improved after 3 months' treatment: probing depth (PD) (mean) at baseline was 4.3 and after 3 months' treatment improved to 3.2 (P=0.001), clinical attachment level (CAL) (mean) was 4.6 and changed to 3.7 (P=0.01), bleeding on probing (BOP %) changed from 64% to 33% (P=0.001), and Plaque index (Pi) changed from 49% to 25% (P=0.001). hs-CRP level was different between the patients'group (pre treatment) and the healthy volunteers: 2.97+/-0.58 mg/L vs. 0.25+/-0.14 mg/L (P=0.00002). After completing 3 months' treatment, hs-CRP levels were decreased from 2.97+/-0.58 mg/L to 2.3+/-0.7 mg/L (P=0.009). CONCLUSIONS: Periodontitis is an infectious condition that may be an insidious cause of chronic inflammation and may be a risk factor for future cardiovascular disease. Treating periodontitis improved inflammation, and might be used as an important prevention tool for cardiovascular disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".