Breast complaints and cancer: age stratified predictors of risk from a prospective database.
Bibliographic record
Abstract
Abstract Abstract #4086 Surgical clinics are presented with a variety of breast complaints. This study looks at major reasons for referral and correlates them to the likelihood of a benign or malignant disease.
 Data comes from a prospective database of new referrals for surgical assessment. Male patients and presentations of recurrent cancer were excluded. Reasons for presentation were sorted into 7 categories defined as breast asymmetry, palpable mass, non-inflammatory skin changes, inflammatory changes, nipple discharge, nipple changes, pain or abnormal imaging. The dominant complaint was applied. Some patients are included twice if they had another problem in the opposite breast, or a second presentation of a new problem. Patient demographics were recorded and all patients were followed to a benign or malignant diagnosis.
 Chi-square testing, odds ratios, and confidence intervals were used for the categorical data. Fisher's exact test was employed for categories with a low cell count.
 Three hundred and ninety of 1050 patients were found to have breast cancer. The most common presentations of malignancy were a palpable mass and abnormal imaging. Less common presentations that also predicted cancer were persistent inflammation, skin changes, and nipple changes. Breast pain (p=0.00001), breast asymmetry (p=0.00001), and nipple discharge (p=0.00001) were significantly correlated with benign disease. The most common diagnosis varied significantly with age. Fibroadenomas were most common in young women, while cysts were frequent in the peri-menopausal group (p=0.0001). A new mass in a woman over 65 years old was malignant 83% of the time (p=0.000001).
 Reason for referral can be significantly correlated with a benign or malignant diagnosis. Breast asymmetry, pain, and nipple discharge were associated with a benign diagnosis, while a new mass in an older woman was more likely to be malignant than benign. This information can be used to help stratify referrals into high and low risk categories, and identify patients for expedited assessment. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4086.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".