Principles of evidence‐based management using stage I–II melanoma as a model
Bibliographic record
Abstract
Evidence-Based Medicine (EBM) is the practice of integrating best research evidence with clinical expertise and patent values. 1 The term, Evidence-Based Medicine, was named in 1992 by a group led by Gordon Guyatt at McMaster University in Canada. The practice of EBM arose from the awareness of: 1 the daily need for valid information pertinent to clinical practice; 2 the inadequacy of traditional sources, like textbooks, for such information; 3 the disparity between clinical enhancing skills and declining up-to-date knowledge and eventually, clinical performance; and 4 the inability to spend more time in finding and assimilating evidence pertinent to clinical practice. EBM simply emphasizes three As: Access, Appraisal and Application. Access requires refining a clinical question into a searchable term and an answerable question and using search engines to track down the information. Appraisal is using epidemiological principles and methods to critically review evidence for its validity and applicability. Application is integrating the critically appraised evidence with clinical expertise and each patient's unique situation. The outcomes following such practices are then assayed. The last step involves evaluating the effectiveness and efficiency in executing the first two As and seeking ways for improvement. In this article, we describe the concept and steps of practising EBM and utilize melanoma as an example to illustrate how we integrate the best evidence to outline the management plan for stage I-II melanoma.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".