131 Difficulties faced by early career researchers engaged in overdiagnosis research and solutions for overcoming them
Bibliographic record
Abstract
Overdiagnosis is a counterintuitive topic that challenges aspects of conventional medicine, the intuitive belief in early detection, and society’s deep faith in medical technology. It goes against cultural norms such as ‘more is better’, ‘knowledge is power’ and ‘experts know best’. Researchers involved in this space may therefore encounter obstacles to conducting scholarly work and challenges to communicating their findings. These difficulties may also carry personal costs and impediments to professional progress. While not unique to this field of research, resistance to conducting and disseminating overdiagnosis research is frequent and may be severe. This session will make use of first-hand experiences of researchers working in this area, and, through discussion, propose practical solutions to mitigate and safeguard against adverse consequences when conducting overdiagnosis research. The format of this workshop will involve short presentations on and discussion of: Sharing personal experiences; Potential solutions to challenges encountered, and steps to: minimize the risk of adverse academic, personal and professional costs; and maintain engagement in academic discussion and evidence based health care. Learning Objectives By the end of the session, participants will have - Increased awareness of academic, personal and professional difficulties and costs encountered when undertaking research on overdiagnosis; and be able to – Identify drivers of resistance to overdiagnosis research; Outline possible solutions to challenges encountered at individual and system levels; and Form support systems
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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 | 0.128 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".