A Review of the Presentation of Overdiagnosis in Cancer Screening Patient Decision Aids
Bibliographic record
Abstract
Introduction. Patient decision aid (PDA) certification standards recommend including the positive and negative features of each option of the decision. This review describes the inclusion of concepts related to overdiagnosis and overtreatment, negative features often ambiguously defined, in cancer screening PDAs. Methods. Our process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We reviewed 1) current systematic reviews of decision aids, 2) the Ottawa Hospital Research Institute Decision Aid Library Inventory, and 3) a web-based, gray literature search. Two independent reviewers identified and evaluated PDAs using content analysis. Reviewers coded whether overdiagnosis/overtreatment was described as 1) detecting cancer that would not lead to death, 2) detecting cancer that would not cause symptoms, and/or 3) a potential harm or consequence of screening. Coding discrepancies were resolved through consensus. Results. A total of 904 records (e.g., articles, PDAs) were reviewed and 85 PDAs were identified: prostate ( n = 36), breast ( n = 26), lung ( n = 10), colorectal ( n = 10), and other ( n = 3). Sixty-seven PDAs included concepts related to overdiagnosis/overtreatment; 57 (67.1%) used a term other than overdiagnosis/overtreatment, 23 (27.1%) used the specific term “overdiagnosis,” and 13 (15.3%) used “overtreatment.” PDAs described overdiagnosis/overtreatment as a potential harm or consequence of screening ( n = 62) and/or a detection of a cancer that would not cause symptoms (n = 49). Thirty-six described overdiagnosis as the detection of a cancer that would not result in death. Twenty PDAs described the probabilities associated with overdiagnosis/overtreatment. Conclusions. Over three quarters of cancer screening PDAs addressed concepts related to overdiagnosis/overtreatment, yet terminology was inconsistent and few included probability estimates. Consistent terminology and minimum standards to describe overdiagnosis/overtreatment would help guide the design and certification of cancer screening PDAs.
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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 | 0.026 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".