3 The importance of sharing information on overdiagnosis for decision making? Experiences and perspectives from different countries
Bibliographic record
Abstract
Overdiagnosis, a known consequence of screening, is not an easy concept to grasp. Clinicians, who should be aware and understand its impact on healthcare decisions, often do not even recognize its existence. The same can be said for individuals that play key roles in the administration of health care or make policy. Raising concerns related to overdiagnosis can impact the relationship between well-intentioned clinicians and their patients or draw criticism to physicians who wants to foster change and limit the harms that health systems can inadvertently cause or contribute to. The Canadian Task Force on preventive health care (CTFPHC) develops clinical practice guidelines that supports primary care providers in delivering preventive health care. We will share the work the CTFPHC is doing to better understand what matters to patients when facing screening decisions. We will describe our systematic reviews reporting on patient’s values and preference and our iterative process that uses focus groups of patients to determine the importance of different outcomes, one of them being overdiagnosis. An example of how confusion about the meaning or the extent of overdiagnosis played a role in a misunderstanding between the Task force and a specific group of physicians will be discussed. We also wish to foster a discussion about how to define and explain overdiagnosis when there is no diagnosis (e.g. risk of fragility fracture). Experiences from other countries will also be discussed. Objectives To share experiences and strategies for sharing information about overdiagnosis and define an agenda for research on that topic. Method There will be four short presentation all related to the overarching theme. Then participants in the audience will be asked to split off into discussion groups to accomplish different tasks Try themselves to explain overdiagnosis (different scenarios will be provided: individual patient, groups, policy maker, learners, collegues, the head of a hospital). We will then ask them to reflect on the difficulties they encountered and about what strategies were helpful or not. Define what they see as the next steps from a research perspective to understand how to best communicate overdiagnosis? Reflect on possible strategies to influence policy? Results Participants will discuss and share ideas. The major themes emerging from that group knowledge will be shared through social media and possibly a blog or a letter to the editor. Conclusions Overdiagnosis is not an easy concept to grasp and even if awareness has increased, there are still many groups of people that have not heard of or simply don’t understand that concept. We will share and deepen some of the thinking happening to counter this state of fact.
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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 | 0.127 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".