Surgical Pathology–Based Outcomes Assessment of Breast Cancer Early Diagnosis
Bibliographic record
Abstract
OBJECTIVE: To develop breast cancer outcomes data relating pathologic tumor variables at diagnosis with clinical method of detection. DESIGN: Anatomic pathologists assessed 30 consecutive breast cancers at each institution, resulting in an aggregate database of 4232 breast cancers. SETTING: Hospital-based laboratories from the United States (98%), Canada, Australia, and Belgium. PARTICIPANTS: One hundred ninety-nine laboratories in the 1999 College of American Pathologists Q-Probes voluntary quality improvement program. MAIN OUTCOME MEASURES: Pathologic variables indicative of favorable outcomes included percentage of carcinomas detected at the in situ stage, tumors < or = 1 cm in diameter, and invasive cancers with lymph nodes negative for metastases. RESULTS: All outcomes measures, including percent in situ carcinomas (26.9% vs 13.8%), tumor size < or = 1 cm (57.8% vs 36.5%), and lymph node-negative status (77.8% vs 64%), were more favorable when tumors were detected by screening mammography (P <.001) compared to all other detection methods. CONCLUSIONS: This study demonstrates an opportunity for pathologists to develop outcomes information of interest to health care organizations, providers, patients, and payers by integrating routine oncologic surgical pathology and clinical breast cancer detection data. Such readily obtained interim outcomes data trended and benchmarked over time can demonstrate the relative clinical efficacy of preventive breast care provided by health care systems long before mortality data are available.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".