The Prevalence of Periodontal disease by Community Periodontal Index in type II-diabetic and non-diabetic patients
Bibliographic record
Abstract
Background: Patients with type II- Diabetes Mellitus show macrovascularµvascular complications including Periodontitis. Aim: To assess periodontal disease prevalence by Community Periodontal Index(CPI)in type II-diabetic and non-diabetic patients. Study design: Cross-sectional comparative study. Place and duration: Dental Out-patient Department at Madina Teaching Hospital, Faisalabad in three months duration. Methodology: CPIprobe (WHO probe) was used to record Community periodontal Index in seventy type-II diabetic and seventy non-diabetic subjectsfor assessment the periodontal disease.The data was collected and SPSS Version 22was used foranalyzing the data. Results: Mean age was 56 years (range 31- 73) diabetic and 46 years (range 20-67) in non-diabetic group. There were more male subjects in both groups. In the non-diabetic group, subjects with good oral hygiene were 13(18.6%). In the diabetic subjects 30(7.14%) had code 3 and 9(2.14%) had code 4. There were 20(4.76%) diabetic subjects and17(4.05%) nondiabetic subjects with missing teeth. Conclusion: Within the study population and limits, diabetic subjects are more likely to have severe periodontal destruction so regular periodontal assessment and treatment must be a part of their management protocol. Keywords: Periodontal disease, Community Periodontal Index, Diabetic mellitus type-II
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".