Combined Performance of Physical Examination, Mammography, and Ultrasonography for Breast Cancer Screening among Chinese Women: A Follow-Up study
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine which combination of physical examination (pe), mammography (mam), and ultrasonography (us) would optimize breast cancer detection in China. METHODS: We conducted a trial of screening with pe, mam, and us among Chinese women 25 years of age and older. All initial screenings using the three modalities were completed within 30 days of each other, and subjects were followed approximately 1 year later. The performances of the three screening methods used alone, in parallel, or in series were compared. Data were analyzed using exact confidence intervals (cis) and the McNemar test. RESULTS: Between March 2009 and July 2011, 3028 eligible women completed all study examinations. At a mean follow-up of 1.3 years, 33 breast cancers were identified in the study population. Mammography detected 28 cancers; us, 24 cancers; and pe, 22 cancers. During the follow-up period, 2 false-negative cases occurred clinically. The highest sensitivity for breast cancer screening (93.9%) was achieved by paralleling mam with us, but came at the cost of a higher recall rate (12.15%). Using us alone at the first stage, followed by mam when indicated, offered high specificity (99.4%) and the lowest recall rate (1.82%), which were not reached at the expense of sensitivity (84.8%). Used in series, us and mam achieved a sensitivity similar to that for the same modalities used in parallel (McNemar p > 0.05). CONCLUSIONS: Taking limited health resources into consideration, the strategy of screening with us alone at the first stage, followed by mam when indicated, may optimize breast cancer detection in most regions of China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".