Bibliographic record
Abstract
After some decades of contention, one can almost despair and conclude that (paraphrasing) "the mammography debate you will have with you always." Against that sentiment, in this review I argue, after reflecting on some of the major themes of this long-standing debate, that we must begin to move beyond the narrow borders of claim and counterclaim to seek consensus on what the balance of methodologically sound and critically appraised evidence demonstrates, and also to find overlooked underlying convergences; after acknowledging the reality of some residual and non-trivial harms from mammography, to promote effective strategies for harm mitigation; and to encourage deployment of new screening modalities that will render many of the issues and concerns in the debate obsolete. To these ends, I provide a sketch of what this looking forward and beyond the current debate might look like, leveraging advantages from abbreviated breast magnetic resonance imaging technologies (such as the ultrafast and twist protocols) and from digital breast tomosynthesis-also known as three-dimensional mammography. I also locate the debate within the broader context of mammography in the real world as it plays out not for the disputants, but for the stakeholders themselves: the screening-eligible patients and the physicians in the front lines who are charged with enabling both the acts of screening and the facts of screening at their maximally objective and patient-accessible levels to facilitate informed decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".