Periodontal Inflammatory Conditions Among Gutka Chewers and Non‐chewers With and Without Prediabetes
Bibliographic record
Abstract
BACKGROUND: It is known that gutka chewing jeopardizes periodontal health; however, severity of periodontal inflammation in gutka chewers with and without prediabetes remains unknown. The aim of this study is to investigate the association of periodontal inflammatory conditions with gutka chewing and prediabetes. METHODS: In this cross-sectional study, the effect of gutka use on periodontal health is investigated among 44 individuals with prediabetes and 44 without prediabetes. Demographic information regarding age, sex, duration of prediabetes, and gutka-chewing habits was collected using a questionnaire. Periodontal inflammatory conditions (plaque index [PI], bleeding on probing [BOP], probing depth [PD], marginal bone loss [MBL]) and fasting blood glucose levels (FBGLs) were recorded. Group differences in periodontal inflammatory parameters were tested using univariate and multivariable analyses (α ≤5%). RESULTS: Periodontal inflammatory parameters (PI, BOP, and PD) were significantly higher in individuals with prediabetes irrespective of gutka-chewing habit (P <0.05). Odds of periodontal inflammation in individuals with prediabetes were nine times higher than in healthy controls (95% confidence interval [CI] = 3.4 to 23.6). Gutka chewing alone, chewing among individuals with prediabetes, and chewing among healthy controls did not significantly increase the odds of periodontal inflammatory conditions. Individuals with prediabetes were significantly more likely to have periodontal inflammation than individuals without prediabetes even after controlling for sex and gutka chewing (odds ratio = 13.2; 95% CI = 4.3 to 40.7). CONCLUSION: In medically healthy individuals, periodontal inflammatory conditions are worse in gutka chewers compared to non-chewers; in patients with prediabetes, the severity of periodontal inflammation is governed by hyperglycemia when compared to habitual gutka usage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".