35 How a guideline recommendation can reduce the overdiagnosis of osteoporosis: an example from the Canadian task force on preventive health care
Bibliographic record
Abstract
Seminar Proposal Guideline groups can play an important role in preventing overdiagnosis and its consequences. The Canadian Task Force on Preventive Health Care develops clinical practice guidelines to support the delivery of primary health care. In this seminar, we will share the work of the Task Force to help prevent the overdiagnosis of osteoporosis. Using the example of a guideline on primary prevention of fragility fractures, participants will discover a new approach to preventing overdiagnosis of ‘osteoporosis’, a surrogate for risk of a future fracture. In screening to prevent fragility fractures, overdiagnosis occurs when individuals are correctly classified or labeled as being at high risk of fracture but would never have known this nor experienced a fracture and may therefore undergo further assessments or preventive pharmacotherapy without benefit. After this seminar, participants will be able to: -Recognize the role of guideline developers in preventing Overdiagnosis. -Explain how we can conceptualize overdiagnosis when screening for risk factors for disease and not diseases per se. -Give concrete examples of how a guideline can address overdiagnosis. Method This seminar will be interactive, and use lectures and small groups. We will review the concept of overdiagnosis in the context of screening to prevent fragility fractures and how to estimate its magnitude. The discussion will then focus on overdiagnosis in the context of predicting fracture risk. This will expand on the harms of labeling and of using a threshold value for bone mineral density (BMD) as a surrogate marker of disease and how giving a disease label to a risk factor contributes to overdiagnosis. As an alternative to dichotomization via the use of arbitrary thresholds, we will use an example from a 2023 guideline to show how a recommendation can decrease labeling and thus overdiagnosis. In groups, participants will be asked to reflect on how guideline recommendations can increase or decrease overdiagnosis. We will provide contrasting examples of recommendations from various guideline groups to stimulate the small group session. Results Participants will be able to better evaluate aspects of guideline recommendations that can help to prevent overdiagnosis. Conclusions The harm of overdiagnosis is a concept that guideline developers must consider in their work. The goal of this seminar is to foster discussion about aspects of guideline recommendations that can help to prevent overdiagnosis.
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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 | 0.037 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".