Bibliographic record
Abstract
Evidence-based medicine has evolved from the need of solving clinical problems. In contrast to the traditional paradigm of clinical practice, evidence-based medicine acknowledges that intuition, clinical experience, and pathophysiologic rationale are not sufficient for making the best clinical decisions. Although evidence-based medicine recognizes the importance of clinical experience, it includes the evaluation of evidence from clinical research and the integration of patients' values, preferences, and actions for best clinical decision-making. To optimize this process, evidence-based medicine advocates that a formal set of rules must accompany training and clinicians' common sense to interpret and apply evidence from clinical research results effectively. We describe the critical appraisal of studies related to prognosis and therapy or prevention building on an example relevant for the clinical orthopaedist. Based on the example, the authors describe how clinicians can apply measures of association and of intervention effects to their practice and patient care. The authors conclude with describing the appraisal of systematic reviews, their application to the development of practice guidelines, and the process of guideline development and recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 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".