Public–Medicine Dissonance: Why in a World of Evidence-based Medicine?
Bibliographic record
Abstract
The evolution of medicine is quite remarkable and astounding. Modern medicine is successfully treating or providing long-term control of conditions which in the not-so-distant past were lethal or resulted in permanent disability. The strong emphasis on evidence-based medicine in today's medical profession has led to a more organized approach toward evaluating the safety and efficacy of new medical treatments. Despite attempts to meet the complex needs of an ever-aging population, an almost cynical or inherent distrust of physicians in general and their medical claims is being increasingly noted. For many physicians this has led to an uncomfortable sense of professional frustration as doubt is cast on themselves or the medical profession in general when the expectations and goals of patients or their families are not achieved. The causes of this apparent malady of contemporary medicine are myriad and may be explored from various perspectives, depending on the particular issue. To understand better the issues and challenges involved, today's medical practitioner needs to be aware of the complex mix of organizational, professional, ethical, and at times anthropological perspectives contributing to this dissonance between medical professionals and the public. Improving our insight into the forces at work in this dissonance will help medical professionals improve medical services to the public and contribute to the preservation of medicine's admirable historical legacy.
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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 | 0.049 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".