Effect of Glycemic Control on Self‐Perceived Oral Health, Periodontal Parameters, and Alveolar Bone Loss Among Patients With Prediabetes
Bibliographic record
Abstract
BACKGROUND: The effect of glycemic control on severity of periodontal inflammatory parameters in patients with prediabetes is unknown. The aim of the present study is to assess the effects of glycemic control on self-perceived oral health, periodontal parameters, and marginal bone loss (MBL) in patients with prediabetes. METHODS: A total of 303 individuals were included. Hemoglobin A1c (HbA1c) and fasting blood glucose levels (FBGLs) were recorded. Participants were divided into three groups: 1) group A: 75 patients with prediabetes (FBGLs = 100 to 125 mg/dL [HbA1c ≥5%]); 2) group B: 78 individuals previously considered prediabetic but having FBGLs <100 mg/dL (HbA1c <5%) resulting from dietary control; and 3) control group: 150 medically healthy individuals. Self-perceived oral health, socioeconomic status, and education status were determined using a questionnaire. Plaque index (PI), bleeding on probing (BOP), probing depth (PD), and clinical attachment loss (AL) were recorded. Premolar and molar MBLs were measured on panoramic radiographs. RESULTS: Periodontal parameters (PI, BOP, PD, and AL) (P <0.01) and MBL (P <0.01) were worse among individuals in group A than those in group B. Self-perceived gingival bleeding (P <0.001), pain on chewing (P <0.001), dry mouth (P <0.001), and oral burning sensations (P <0.05) were worse among patients in group A than those in group B. There was no difference in periodontal parameters, MBL, and self-perceived oral symptoms among patients with prediabetes in group B and healthy controls. CONCLUSIONS: Self-perceived oral health, severity of periodontal parameters, and MBL are worse in patients with prediabetes than controls. Glycemic control significantly reduces the severity of these parameters as well as the state of prediabetes in affected individuals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".