Public Awareness on Oral Cancer: A Population- Based Study in Asturias
Bibliographic record
Abstract
OBJECTIVE: Through awareness campaigns, we can change the patient's abilities to detect oral cancer at an early stage and their ability to seek help. To focus these campaigns, we need to know the level of knowledge of the population and its interest in learning about this disease. The aim of this study was to assess the level of oral cancer awareness in Asturias and the interest of the population in learning about this pathology. METHODS: A representative community-based survey was carried out online using Google Forms®. Responses were transferred to a Microsoft Excel and analysed using the R-program. The relationship between two qualitative variables was studied using Pearson's Chi-square test or Fisher's test. Univariate and multivariate logistic regression models were used to determine which factors are associated with knowledge of oral cancer. RESULT: We found that those having over 50 years and being health professionals are more likely to know about the existence of oral cancer. Almost 85.1% of participants mentioned tobacco as a risk factor, only 39.8% identified alcohol. The ulcer was the most frequently recognized alarm sign (70.6%). The primary care physician was chosen as the first option for consultation by the 56.5% of the sample. Only 12.4% of the participants reported knowing how to self-examine their mouth. The number of views of a video of how-to self-inspection oral cavity displayed at the end of the questionnaire increased in a 39.38% during our study period. CONCLUSION: This survey showed a worrying lack of awareness and knowledge about oral cancer among the population of Asturias, especially among those under 50 years old. The interest shown in increasing their knowledge, give us hope in the success of future awareness campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".