What you see is not always what you get: Radiographic-pathologic discordance among benign breast masses
Bibliographic record
Abstract
The differential diagnosis for benign breast masses is broad and ranges from common lesions like fibroadenomas to rare masses like breast hamartomas. Fibroadenomas are proliferative benign masses made up of fibroglandular tissue. Hamartomas are neoplasms comprised of different tissues that are endogenous to the area where they originate. Breast hamartomas specifically, are rare, benign slow growing tumours comprised of fibrotic stroma, adipose, glandular tissue, and epithelial components. Both lesions are painless, firm, and are typically palpable on clinical exam. Given their similarities in composition, diagnosing these masses can be challenging, but may be confirmed with ultrasonography, mammogram, computed tomography, magnetic resonance imaging, or via histological specimen. Once diagnosed, surgical excision is the preferred treatment option. We present a 33-year-old woman with a large left breast mass that gradually increased in size and provide a review of the current literature regarding the challenge of distinguishing between breast fibroadenomas and hamartomas.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".