Bibliographic record
Abstract
Since its formal introduction in 1991, evidence-based medicine/practice has received considerable attention. Defined as “the conscientious, explicit, and judicious use of best evidence in making decisions about the care of individual patients,”1 evidence-based practice embraces the integration of best research evidence, clinical expertise, and patient values.2 Clinicians are active participants not only in applying their expertise, but also in seeking out and interpreting research evidence. To allow the optimal transfer of information from research report to clinical practice, researchers must present their findings in an easy-to-understand format that provides the maximum amount of information efficiently. When interpreting the results from studies investigating the merits of competing therapeutic interventions, the reliability or validity of clinical measurements, or the causal association of putative risk factors, clinicians and researchers are interested in the answers to 2 important questions: (1) Are the results likely due to chance? and (2) Are the findings clinically important? The former question considers statistical significance, and the latter question addresses clinical significance. Statistical significance is dictated by tradition, with a critical P value of .05 typically being the requisite minimal value. Statistical significance is influenced by sample size, sample variability, and the magnitude of the observed effect. In contrast to the arbitrary standard for statistical significance, clinical importance is influenced by personal beliefs, risk of an adverse event, cost, and the feasibility of providing the intervention, test, or measure in practice. Because clinicians—and researchers for that matter—are likely to have different opinions concerning the magnitude of a clinically important difference, it is essential that authors provide their results …
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 | 0.034 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".