From Data to Evidence: Evaluative Methods in Evidence-Based Medicine
Bibliographic record
Abstract
The amount of published information is increasing exponentially, and recent technologic advances have created systems whereby mass distribution of this information can occur at an infinite rate. This is particularly true in the broad field of medicine, as the absolute volume of data available to the practicing clinician is creating new challenges in the management of relevant information flow. Evidence-based medicine (EBM) is an information management and learning strategy that seeks to integrate clinical expertise with the best evidence available in order to make effective clinical decisions that will ultimately improve patient care. The systematic approach underlying EBM encourages the clinician to formulate specific and relevant questions, which are answered in an iterative manner through accessing the best available published evidence. The arguments against EBM stem from the idea that there are inherent weaknesses in research methodologies and that emphasis placed on published research may ignore clinical skills and individual patient needs. Despite these arguments, EBM is gaining momentum and is consistently used as a method of learning and improving health care delivery. However, if EBM is to be effective, the clinician needs to have a critical understanding of research methodology in order to judge the value and level of a particular data source. Without critical analysis of research methodology, there is an inherent risk of drawing incorrect conclusions that may affect clinical decision-making. Currently, there is a trend toward using secondary pre-appraised data rather than primary sources as best evidence. We review the qualitative and quantitative methodology commonly used in EBM and argue that it is necessary for the clinician to preferentially use primary rather than secondary sources in making clinically relevant decisions.
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 | 0.604 | 0.780 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier 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".