Breast Specimen Measurement Methodology and Its Potential Major Impact on Tumor Size
Bibliographic record
Abstract
OBJECTIVE: Pathologic tumor size assessment highly depends on the gross specimen size once microscopic cancer size exceeds its macroscopic size, in particular if the dimension along the plane of sectioning is the greatest. We hypothesize that the method by which the specimen size is estimated can yield significantly different tumor size measurements and thus affect breast cancer staging and treatment. METHODS: The size in the plane of sectioning of 50 lumpectomies over 4 cm was examined by 5 methods: measured grossly in the fresh state and postfixation, and calculated from the gross measurements by 3 different methods. For 15 mastectomies, we measured and calculated the span of the middle 4 and 6 slices using 3 methods. RESULTS: < .001). Using the method of adding 0.4 cm per each submitted sequential section yielded the smallest size in most cases. In mastectomies the span of the middle 4 and 6 slices was significantly larger if calculated from the average slice thickness based on the specimen size. CONCLUSION: The method of specimen size measurement has implications in estimation of tumor size and patient management. It is essential that pathologists be aware of the technique used and its limitations. For individual slice thickness, we highly recommend using the measurements obtained at the time of grossing rather than calculating the average slice thickness from the specimen size.
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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".