Benefits and Harms of Detecting Clinically Occult Breast Cancer
Bibliographic record
Abstract
Over the last few decades there has been an increase in the use of strategies to detect clinically occult breast cancer with the aim of achieving diagnosis at an earlier stage when prognosis may be improved. Such strategies include screening mammography in healthy women, diagnostic imaging and axillary staging in those diagnosed with breast cancer, and the use of follow-up imaging for the early detection of recurrent or metastatic disease. Some of these strategies are established, whereas for others there are inconsistent supportive data. Although the potential benefit of early detection of clinically occult breast cancer seems intuitive, use of such strategies can also be associated with harm. In this commentary, we provide an extended discussion on the potential benefits and harms of the routine and frequent use of screening interventions to detect clinically occult breast cancer and question whether we may be causing more harm than good.
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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 | 0.024 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".