Comparação do código de ética médica do Brasil e de 11 países
Bibliographic record
Abstract
OBJECTIVE: Compare the Code of Medical Ethics of the Federal Council of Medicine of Brazil with codes from 11 different countries, with the purpose of improving the comprehension of their structure and contribute to the achievement of their objectives. METHODS: Codes from five continents and 11 countries: Argentina, Chile, Canada, United States, Portugal, United Kingdom, South Africa, Egypt, China, India, and Australia were studied. Information was obtained from the Internet, by accessing sites of regulatory agencies and medical associations. Codes were described and compared according to information about the setting-up organization, spatial scope, compulsory extent, date of enforcement, organizational rules and auxiliary documents. RESULTS: The codes of ethics studied were: 59% created by the medical regulatory agency of the country, 92% of national scope, 67% compulsive for all physicians and 73% were last updated after the year 2000. A relation between the setting-up organization and the compulsory extent and spatial scope of the codes was observed. Need for systematic updating of the codes was noted. Updating is often carried out through auxiliary documents, however, there may be difficulties in making these contents known. The possibility of organizing the guidelines by topics, each followed by a small text was considered. CONCLUSION: This study presented suggestions for the Code of Medical Ethics of Brazil: conduct a review and an update of the code, organize the guidelines, including explanations and justifications, separate the ethical resolutions and finally improve its divulgation.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".