Evidence-based medicine and precision medicine: Complementary approaches to clinical decision-making
Bibliographic record
Abstract
Evidence-based medicine is widely promoted for decision-making in health care and is associated with improved patient outcomes. Critics have suggested that evidence-based medicine focuses primarily on groups of patients rather than individuals, but often fail to consider subgroup analyses, N-of-1 trials, and the incorporation of patient values and preferences. Precision medicine has been promoted as an approach to individualize diagnosis and treatment of diseases through genetic, biomarker, phenotypic, and psychosocial characteristics. However, there are often high costs associated with personalized medicine, and high-quality evidence is lacking for effectiveness in many applications. For the potential of personalized medicine to be realized, it must adhere to the principles of evidence-based medicine: (1) evidence in isolation is not sufficient to make clinical decisions-patient's values and preferences as well as resource implications must be considered, and (2) there is a hierarchy of evidence to guide clinical decision-making and studies at lower risk of bias are likely to provide more trustworthy findings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".