Prevention and control of oral potentially malignant disorders
Bibliographic record
Abstract
Abstract Oral cavity cancer accounts for approximately 4% of all malignancies and is a significant worldwide health problem. In Southeast Asia, oral cancer account for 40% of all cancers. It is the consensus of health promotion researchers that culturally specific (CS) interventions are important in addressing smoking-related health disparities. Equivocal evidence also exists for tobacco interventions that do not attend to culture specific characteristics, that is, traditional or non-CS interventions. Tobacco use is one of the major public health problems in the world, resulting in 5.4 million deaths every year. Over half of all tobacco consumed in India is smoked as bidi (Indian specific non-filtered cigarette) and about one-fourth of tobacco consumption is in smokeless form. Oral and pharyngeal cancer grouped together is the eighth most common cancer in the world. Two third of these cases occur in developing countries. Tobacco and alcohol are well known risk factors- attributable risk of 75%-95%. It is possible to prevent a substantial proportion of oral cancer even for patients who already have precancerous lesion. There is encouraging evidence from a large primary intervention trial in India that the chance of precancerous lesions and conditions undergoing malignant change is reduced if the patients can be persuaded to curtail their dependence on tobacco (Gupta et al 1992). High risk groups of the population can be identified. Type of screening could be a.Mass Screening b.High risk or selective screening c. Multiphasic screening (health questionnaire, clinical examination, measurements and investigations.) Patients should utilize dental services whatever available. It involves surgical intervention and reconstruction of the tissue destroyed. Based on Ottawa Charter (WHO 1986) there is a range of options for oral health promotion approach to oral cancer prevention. Success depends on political will, inter-sectoral action and culturally sensitive public health messages, disseminated through educational campaigns and mass media initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".