RESPONSE: Re: International Variation in Screening Mammography Interpretations in Community-Based Programs
Bibliographic record
Abstract
Our literature search, as outlined in the “Methods” section of our recent article (1), reviewed MEDLINE peer-reviewed publications up to mid-2002 that included three search terms: “mammography,” “mass screening,” and “biopsy”. The references noted by Onysko et al. in their correspondence were not identified by our search because they were either published in 2003 (i.e., were not in the peer-reviewed literature at the time of our search) or did not use the specified key terms. Screening practices in Canadian breast cancer screening mammography programs may differ from those in the United States in several ways. Indeed, the medical malpractice environment, fiscal incentives, health care structure, and breast cancer screening recommendations differ between the two countries. We strongly suspect that our findings reflect differences in screening performance mainly between U.S. screening mammography programs and screening results from other countries. Our findings are consistent with another recently published article (2), in which screening mammography performance was compared between the United States and the United Kingdom. In that study, the recall rate and negative open-surgical biopsy rates were twice as high in United States settings as they were in the United Kingdom, whereas the cancer detection rates were similar in both countries. We agree with Onysko et al. about the need for standardization of international comparisons of screening program performance. However, the data required for meaningful comparison of screening performance are, unfortunately, not always available in the existing published literature (for example, the type of diagnostic assessment performed or definitions used to calculate outcomes). The conceptual model and brief discussion of the potential reasons for variability among published studies of screening mammography [Table 3 in (1)] may be helpful in making comparisons of screening program performance in future studies.
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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 | 0.010 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.019 |
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".