Preventing overdiagnosis and the harms of too much sport and exercise medicine
Bibliographic record
Abstract
Do I really need this test, treatment or procedure? What are the downsides? What happens if I do nothing? And are there simpler, safer options? These four questions, promoted by Choosing Wisely Canada, featured prominently at the two 2018 conferences, Too Much Medicine in Helsinki, Finland (figure 1), and the sixth annual Preventing Overdiagnosis conference in Copenhagen, Denmark. Over 600 of the world’s leading researchers and thinkers in preventing overdiagnosis came together for two weeks in August 2018 to highlight the problems caused by medical excess and to identify evidence-informed practices to wind back the harms of too much medicine. Figure 1 You can review highlights from the Too Much Medicine symposium on Twitter @TooMuchMed. This education review aims to bring the sport and exercise medicine reader up to date on this topic. An expanded version with additional references and resources is provided in the online supplementary file. ### Supplementary data [bjsports-2018-100039supp001.docx] Too many people are being overdiagnosed, leading to overtreatment and wasted resources that could be better spent preventing or treating genuine illness. While debates about its definition continue, narrowly defined, overdiagnosis refers to deviations, abnormalities, risk factors and pathologies that would never cause symptoms or early death. It relates to problems of overmedicalisation and disease mongering, resulting in what one keynote speaker described as a ‘tsunami of overtreatment’. As a review in BMJ discovered1 (figure 2), many factors drive overdiagnosis and cause harm, including cultural beliefs that ‘more is better’, financial incentives, expanding disease definitions and lowering treatment thresholds. While clinicians have been, and will forever be, challenged to balance Hippocratic notions of beneficence and non-maleficence, the scales are heavily tipped by the underlying influence of industry—such as Big Pharma, divisions of the media more interested in promotion than journalism, and medical journals serving professional rather than public interest. Attempting to summarise discussions …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".