Validity of Self-perceived and Clinically Diagnosed Gingival Status among 12–15-year-old Children in Indonesia
Bibliographic record
Abstract
A few studies have revealed the self-perceived gingival status using questionnaires among children. Perceived health is a crucial factor that has an impact on quality of life. The objective of the study was to assess self-perceived and clinically diagnosed gingival status among children in Indonesia. This was a cross-sectional study of 494 schoolchildren (aged 12-15 years). Periodontal status was recorded using the gingival index (GI) and plaque index (PI) based on the World Health Organization standards. Data were collected through a brief visual, non-invasive clinical oral examination and a self-administered questionnaire. The sensitivity and specificity of self-perceived assessment were calculated using normative assessment as the gold standard. This study showed that self-perceived need for dental treatment showed the highest sensitivity (86% using PI and 85% using GI) and self-perceived swollen gums showed the highest specificity (89% using PI and 88% using GI) for clinically diagnosed plaque (PI cut-off value: 0.74) and gingival problems (GI cut-off value: 0.51). In conclusion, both self-perceived variables showed significant discordance between their respective sensitivity and specificity. Self-perceived information is at a higher-level unawareness that does not reflect the current gingival status. Thus, public health strategies are needed to improve the awareness of better oral health among children by promoting, empowering, and advocating.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".