Conceptualizing Overdiagnosis in Cancer Screening
Bibliographic record
Abstract
The aim of cancer screening is to detect asymptomatic cancers whose treatment will result in extension of life, relative to length of life absent screening. Unfortunately, cancer screening also results in overdiagnosis, the detection of cancers that, in the absence of screening, would not present symptomatically during one's lifetime. Thus, their detection and subsequent treatment is unnecessary and detrimental. This definition of overdiagnosis, while succinct, does not capture the ways it can occur, and our interactions with patients, advocates, researchers, clinicians, and journalists have led us to believe that the concept of overdiagnosis is difficult to explain and, for some, difficult to accept. We propose a dichotomy, the "tumor-patient" classification, to aid in understanding overdiagnosis. The tumor category includes asymptomatic malignant disease that would regress spontaneously if left alone, as well as asymptomatic malignant disease that stagnates or progresses too slowly to be life threatening in even the longest of lifetimes. The patient category includes asymptomatic malignant disease that would progress quickly enough to be life threatening during a lifetime of typical length, but lacks clinical relevance because death due to another cause intercedes prior to what would have been the date of symptomatic diagnosis had screening not occurred. Cancer screening of most organs is likely to result in overdiagnosis of both types. However, the ratio of tumor- to patient-driven overdiagnosis almost certainly varies, and may vary drastically, by organ, screening modality, patient characteristics, and other factors.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".