Oral hygiene practices and risk of oral leukoplakia
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of oral hygiene habits and practices on the risk of developing oral leukoplakia. DESIGN: Case control study. SETTING: Githongo sublocation in Meru District. SUBJECTS: Eighty five cases and 141 controls identified in a house-to-house screening. RESULTS: The relative risk (RR) of oral leukoplakia increased gradually across the various brushing frequencies from the reference RR of 1.0 in those who brushed three times a day, to 7.6 in the "don't brush" group. The trend of increase was statistically significant (X2 for Trend : p = 0.001). The use of chewing stick as compared to conventional tooth brush had no significant influence on RR of oral leukoplakia. Non-users of toothpastes had a significantly higher risk of oral leukoplakia than users (RR = 1.8; 95% confidence levels (CI) = 1.4-2.5). Among tobacco smokers, the RR increased from 4.6 in those who brushed to 7.3 in those who did not brush. Among non-smokers, the RR of oral leukoplakia in those who did not brush (1.8) compared to those who brushed was also statistically significant (95% CL = 1.6-3.8). CONCLUSION: Failure to brush teeth and none use of toothpastes are significantly associated with the development of oral leukoplakia, while the choice of brushing tools between conventional toothbrush and chewing stick is not. In addition, failure to brush teeth appeared to potentiate the effect of smoking tobacco in the development of oral leukoplakia. RECOMMENDATIONS: Oral health education, instruction and motivation for the improvement of oral hygiene habits and practices; and therefore oral hygiene status, should be among the strategies used in oral leukoplakia preventive and control programmes.
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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 | 0.001 | 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".