Optimal assessment of residual disease after neo‐adjuvant therapy for locally advanced and inflammatory breast cancer—clinical examination, mammography, or magnetic resonance imaging?
Bibliographic record
Abstract
PURPOSE: Accurate assessment of residual disease after neo-adjuvant chemotherapy (NEC) for women with locally advanced and inflammatory breast cancer (LABC) is critical for planning surgery. The study's purpose was to prospectively determine the optimal method (clinical examination (CE), mammogram (MG), and magnetic resonance imaging (MRI)) for assessing residual disease after NEC for women with LABC. METHODS: Women with LABC who received NEC and surgery were enrolled. Patient demographics, tumor size as measured by CE, MG, and MRI both before and after NEC, and final pathologic size of tumor were collected. Response to NEC was calculated using RECIST criteria. Paired t-tests and the Pearson correlation were used to compare tumor size on CE, MG, MRI, and final pathology. RESULTS: Forty-eight women with 50 LABC were recruited. Mean pre-NEC tumor size was 8.2, 5.1, and 6.2 cm on CE, MG, and MRI. Mean post-NEC tumor size was 2.4, 4.3, 3.9, and 3.6 cm on CE, MG, MRI, and final pathology. The Pearson correlation co-efficient between post-NEC measurements and pathology was 0.63 (CE), 0.15 (MG), and 0.49 (MRI). CONCLUSION: We found that there was limited correlation between the extent of residual disease after NEC for patients with LABC as assessed by CE, MG, and MRI as compared to final pathology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".