Analysis and Impact of Evidence Based Medicine in the Process of Education and Decision Making in Medical Practice
Bibliographic record
Abstract
Evidence Based Medicine (EBM) is one of the modern systems of collecting, recording and using information and data related to medicine. The research was conducted in 2018 in several health institutions (health centers and hospitals) in Sarajevo Canton. The sample was made up of family doctors and other clinical specialties. The research results suggest the doctors in BiH are familiar with the concept of Evidence Based Medicine (EBM) and in their everyday work they apply relevant contemporary knowledge, national and international guidelines for the treatment and disease therapy. The most significant obstacle to more efficient management of medical information and the implementation of EBM in practice is the perception it would require additional time and incur significant cash expenditures to the doctors. In the future, doctors will increasingly be demanded to use advanced tools and modern techniques supporting them to make the most effective treatments on the basis of their own experience.
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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 | 0.123 | 0.356 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".