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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".