Measuring extent of ductal carcinoma in situ in breast excision specimens: a comparison of 4 methods.
Bibliographic record
Abstract
CONTEXT: Measuring the extent of nonpalpable ductal carcinoma in situ (DCIS) in a breast specimen is challenging but important because it influences patient management. There is no standardized method for estimating the extent of DCIS, although serial sequential sampling with mammographic correlation is considered an accurate method. OBJECTIVE: To estimate the extent of DCIS using various methods and to compare these estimations with the extent as determined by the serial sequential sampling method. DESIGN: A total of 78 primary breast excisions with DCIS were retrospectively reviewed. All specimens had been sampled using the serial sequential sampling method, which involved mapping the location of each block on the sliced specimen radiograph and calculating the extent through 3-dimensional reconstruction. The other measures for estimating extent included (1) calculating size based on areas of calcification, (2) recording the number of blocks involved by DCIS and multiplying that number by 0.3 cm, and (3) measuring the largest extent of DCIS on a single slide. RESULTS: All 3 alternative methods tended to underestimate the DCIS. Discrepancies became more pronounced as size increased. The percentage of cases estimated to within 1 cm of the serial sequential sampling method were 81%, 72%, and 50%, respectively, for the calcification, blocks, and single-slide methods; differences of more than 2 cm were seen in 9%, 8%, and 30% of cases, respectively. CONCLUSIONS: The single-slide method performed poorly and should be used only when DCIS is limited to a single slide. Although the calcification and the blocks methods gave better estimates, both produced substantial underestimates and/or overestimates that could affect clinical decision making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".