Oral cancer and dentists: knowledge, attitudes and practices in a South Colombian context.
Bibliographic record
Abstract
An estimated 36.38% of oral cancer cases in Colombia are lethal. Most cases are diagnosed in late stages, so early detection and control of risk factors would be the most effective tools for prevention. The aim of this study was to use a questionnaire to evaluate knowledge and practices regarding oral cancer among a group of dentists in southern Colombia. A sample of 93 dentists was asked to respond to a confidential survey which was based on prior studies. It was found that one quarter of the respondents knew that squamous cell carcinoma is the most frequent form of oral cancer and that leukoplakia and erythroplakia are the two lesions most probably associated to oral cancer. Most respondents believe they advise their patients adequately about suspicious lesions. Three quarters believe they are prepared to explain the risks of smoking. Over half evaluate their patients' personal history of tumors and less than one quarter evaluate the history of cancer in patients' families. In general, an oral examination is performed for cancer diagnosis and almost all the respondents consider it important to keep up to date. A statistically significant correlation was found between dentists 'belief that they are adequately prepared to perform a physical examination and having attended a "formal course within the past 12 months". This study revealed that dentists' level of knowledge and application of preventive measures are an important part of the public health strategy for reducing the morbidity and mortality for oral cancer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".