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.
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 | 0.115 | 0.641 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".