The Addition of Internists to a Breast Health Program
Bibliographic record
Abstract
With the increases in complexity of care for breast health concerns, there is a growing need for efficient and effective clinical evaluation, especially for vulnerable populations at risk for poor outcomes. The Breast Health Center at Boston Medical Center is a multidisciplinary program, with internists providing care alongside breast surgeons, radiologists, and patient navigators. Using a triage system previously shown to have high provider and patient satisfaction, and the ability to provide timely care, patients are assigned to either a breast surgeon or internist. From 2007 to 2009, internists cared for 2,408 women, representing half of all referrals. Women served were diverse in terms of race (33% black, 30% Hispanic, 5% Asian), language (34% require language interpreter), and insurance status (51% had no insurance or public insurance). Most presented with an abnormal screen (breast examination 54% or imaging 4%) while the remainder were seen for symptoms such as pain (26%), non-bloody nipple discharge (4%), or risk assessment (7%). A majority of final diagnoses were made through clinical evaluation alone (n = 1,760, 73%), without the need for additional diagnostic imaging or tissue sampling; 9% (n = 214) received a benign diagnosis with the aid of breast imaging; 19% (n = 463) required tissue sampling. Only 4% went on to see a breast surgeon. Internists diagnosed 15 incident cancers with a median time to diagnosis of 19 days. Patient and provider satisfaction was high. These data suggest that a group of appropriately trained internists can provide quality breast care to a vulnerable population in a multidisciplinary setting. Replication of this model requires the availability of more clinical training programs for non-surgical providers.
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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 | 0.000 |
| 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".