Community periodontal index of treatment needs and prevalence of periodontal conditions
Bibliographic record
Abstract
BACKGROUND/AIMS: In 1977, the World Health Organization (WHO) proposed a new index, the community periodontal index of treatment needs (CPITN) to evaluate the periodontal treatment needs of populations. The aim of this study is to compare different approaches of recording and presenting the CPITN. METHODS: A sample of 2110 subjects aged 35-44 years were examined between September 1994 and July 1995, throughout the province of Quebec, Canada. For each tooth (3rd molars excluded), the presence of bleeding and calculus, the level of epithelial attachment, and the depth of periodontal pockets were measured. Periodontal pocket depths were measured from the edge of the free gingiva, at 2 sites (mesiovestibular and vestibular), as well as all around the tooth. RESULTS: Only 8.5% of adults had at least one tooth with a 6 mm or deeper periodontal pocket when probing on 2 sites, whereas if probing is done all around the tooth, this percentage is 2.5x higher (21.4%). The partial recording of pocket depths (10 index teeth recommended by WHO, or 2 quadrants chosen at random) resulted in an underestimation of the prevalence of subjects with at least one tooth with a periodontal pocket (CPITN score 3 and 4). Among subjects with at least one tooth with a 6 mm or deeper periodontal pocket, 12% were not detected with the 10 index teeth recording, and 25% go undetected with the measure on 2 quadrants. Finally, using the % of subjects with periodontal pockets overestimates the prevalence of deep pockets compared with using sextants. Indeed, close to 30.0% of sextants have no treatment needs, whereas only 5.2% of subjects are in this category. Similarly, 7.7% of sextants have at least one tooth with a 6 mm or deeper periodontal pocket, yet there are 3x more subjects in this category (21.4%).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".