Estudo da atitude e do conhecimento dos médicos não oncologistas em relação às medidas de prevenção e rastreamento do câncer
Bibliographic record
Abstract
BACKGROUND: New cancer cases are most often diagnosed by non-oncologist physicians. It is therefore essential for all physicians to be aware of cancer preventive practices and use them appropriately with their patients. METHODS: Questionnaires were administered to 120 non-oncologist physicians of various specialties attending the "Faculdade de Medicina do ABC" who deal directly with adult patients. Replies were collected and classified as appropriate or not according to one of these three cancer prevention guidelines: INCA, American Cancer Society and Canadian Task Force. RESULTS: The percentage of replied questionnaires was 58.3% (70 questionnaires). Mean age of physicians was 33.9 years; 57.1% were women and 10% smokers. Most of the current preventive practices adopted by the participating physicians (45.72% to 100%) regarding the most common and preventable tumors (breast, cervix, prostate, colon and rectum and non-melanoma skin cancer) did not agree with any of the guidelines mentioned above. When questioned about possible impediments for the appropriate practice of cancer prevention, 82.86% reported absence of health education agents working with the population, 77.14% scarceness of knowledge or training concerning prevention, and 70.15% lack of financial support for ordering tests. Frequently, whenever there was disagreement between the guidelines and the physician's current practices, preventive tests were ordered in excess of those recommended by the guidelines. CONCLUSIONS: Physicians had a tendency to order excessive laboratory tests, an action which can be related to lack of knowledge and to divergence among guidelines. A more intensive educational effort regarding cancer prevention, directed towards teaching physicians in training, seems to be warranted.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".