What you see is not always what you get: Radiographic-pathologic discordance among benign breast masses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it