Bibliographic record
Abstract
To the Editor, We thank Dr. Lee's further comments1 on our previous work of the mammographic screening in Taiwan.2 Dr. Lee had questioned the clinical benefits of mammographic screening as a previous Canadian study with a 25-year follow-up period had shown that no difference in breast cancer mortality was observed between the mammography and control arms.3 In a comment,4 professor Kopans, one of the experts called on in 1990 to review the quality of the Canadian mammographic screening, attested to the fact that the overall screening quality of the Canadian study was poor because: 1) second hand machines were used to save money, 2) technologists were not taught to position the breasts in the machines properly, and 3) their radiologists had no specific training for mammographic interpretation. Moreover, the Canadian study had violated the fundamental rules of randomized controlled trial (RCT) that more women with lumps or even advanced breast cancer were arbitrarily allocated into screening group in order to be sure to get a free mammogram,3,4 resulting in only one-third of cancers being detected by mammography alone, which was far less than most of the other screening studies,5 and poorer survival than that of control group. On the contrary, in a well-qualified Swedish RCT study,6 a 30% reduction of breast cancer mortality can be achieved in the mammographic screening group. Also, professor Chang7 had reported a 33% breast cancer mortality reduction from the biennial mammographic screening in Taiwan. With state-of-the-art digital mammographic machines (especially digital tomosynthesis),8 continuous education of the screening radiologists and technologists with medical audits of the mammographic interpretations,9 the results of Taiwan's mammographic screening have reached the level of ACR recommendations, which does make a significant clinical benefit and improve women's health in Taiwan. Conflicts of interest The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".