Exploring the continuum: medical information to effective clinical practice*. Paper I: the translation of knowledge into clinical practice
Bibliographic record
Abstract
This paper investigates the translation of medical information into clinical practice and the role of thoughtful dissent by exploring the influence of sociological factors on change, the impact of evidence-based medicine (EBM), and the role of industry. Changing practice related to hormone therapy for menopausal and post-menopausal women provides context for this discussion. Medical change involves diffusion of ideas to potential users and ongoing reconciliation of new information with old ideas; this process is influenced by sociological factors including values and experiences, interpersonal relationships and local context. While EBM has alerted doctors to the importance of high quality research and theoretically provides a tool for translating research into practice, there are important problems with its application: (1) it has resulted in a reductionist approach to research and illness; (2) there is a considerable gap between research findings and the complex environment of clinical practice; and (3) EBM has been appropriated by experts, thus corporately developed 'standard-of-care' documents have become instruments of external regulation, and EBM has ceased to be a tool in the hand of individual clinicians. In addition, industry impacts the translation of knowledge by significantly influencing academia, researchers, medical publications, consensus conferences, and practising doctors. While questioning doctrinaire practices or directives is a daunting prospect for individual clinicians, the translation of knowledge into practice and evolution of medical thought is dependent on the progressive role of thoughtful dissent.
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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 | 0.045 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.099 |
| Scholarly communication | 0.038 | 0.052 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".