Bibliographic record
Abstract
the Answer? Like many clinicians, I often feel overwhelmed by the explosion of scientific knowledge. I know that I cannot possibly keep up with the medical literature and synthesize all of the information available to me in an efficient and practical manner. The electronic medical record, with all of its promised efficiencies, has really yet to materialize in a practical sense. Added to these woes is a natural reaction to bristle at yet another new term in our medical lexicon, namely, evidence-based medicine (EBM). What is the etiology of this conveniently labeled phenomenon? How might it help us to achieve our long sought-after goal of providing our patients with higher quality care at a lower economic burden? What are the future prospects for this field in general? I am not aware of any published reports formally outlining the etiology of EBM. By most accounts, one can trace its origins to a group of dedicated clinician teachers at McMaster University in Ontario, Canada, in the late 1980s. At McMaster, a core group of medical school faculty was trying to demonstrate that medical decisions should be
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 | 0.072 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".