Bibliographic record
Abstract
Despite evidence-based medicine's (EBM's) significant evolution and maturation from its revolutionary origins to its current form as the preeminent means of practicing medicine, there are still good reasons to be unsatisfied with EBM. This essay explores two important new developments in EBM: recently articulated accounts of the scientific basis of EBM, and the related writings of the GRADE Working Group to create standards for interpretation of the medical literature and evaluation of recommendations. A review of Karanicolas, Kunz, and Guyatt's (2008) three-step articulation of EBM's scientific basis demonstrates that the supposed soundness of each principle is not attributable to its scientific status; instead, the normative language of each principle highlights EBM's grounding in an only partially articulated philosophical framework. The GRADE Working Group's effort similarly relies on credibility, consensus, and trust in its defense and justification of EBM. These recent developments in EBM reveal that if the clinical research literature is to be informative or foundational to the enterprise of health care, much work needs to be done to secure its trustworthiness and integrity. An agenda for examining trust and trustworthiness in the context of health research is proposed.
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 | 0.636 | 0.872 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.009 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.038 | 0.049 |
| Open science | 0.010 | 0.032 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".