Evaluation of the Salivary Matrix Metalloproteinase-9 in Women With Polycystic Ovaries Syndrome and Gingival Inflammation: A Case-Control Study
Bibliographic record
Abstract
Background Polycystic ovary syndrome (PCOS) is an endocrine disease of women of reproductive age that impacts their oral and systemic well-being. This study aimed to compare the gingival inflammation indices and matrix metalloproteinase-9 (MMP-9) of non-obese women with PCOS. Materials and methods This is a case-control study in which 78 women were referred to the Babol Clinic Hospital in Northern Iran between 2018 and 2019. They were divided into three groups: 26 women with PCOS and gingivitis, 26 women with PCOS with no gingivitis, and 26 women with no PCOS and no gingivitis as a control group. After recording the anthropometric and demographic variables, fasting saliva samples were taken from all participants before any periodontal intervention. These samples were transferred to Babol Molecular Cell Research Center under highly guaranteed cold-chain conditions to measure the serum levels of MMP-9. Periodontal status was evaluated for Gingival Index (GI), Plaque Index (PI), and Bleeding on Probing (BOP). Analysis of variance was used to compare the mean results for these indices. The significance level was considered when p ≤ 0.05. Results All the gingival indices were significantly higher for women with PCOS with gingivitis compared to the results for women from the other two groups. Similarly, women with PCOS showed high salivary MMP-9 levels but were within the normal reference ranges. Conclusion The gingival indices (GI, PI, and BOP) and salivary MMP-9 are higher in women with PCOS, regardless of the gingival status.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".