Think Globally, Start Locally: Value-Based Breast Cancer Care for Newly Diagnosed Patients in A Safety-Net Medical Center
Bibliographic record
Abstract
Introduction We assessed the efficacy of a multidisciplinary, patient-focused approach emphasizing appropriate use of medical resources among a population of breast cancer patients at our safety-net hospital. Methods A multidisciplinary program coordinated and provided value-based care. Surgery, oncology, and navigation were physically co-located. Real time decisions were made by medical and surgical oncologists. Focused institution-specific protocols initiated in 2018, advised against four specific cancer resources that our team had determined as lower-value: imaging tests for indications not recommended in NCCN guidelines, inappropriate Oncotype Dx testing, radiation for patients ≥65 years with stage I hormone-positive disease, and administration of pertuzumab and neratinib as adjuvant therapy in HER2+ breast cancer patients. Time to treatment and rates of use of these resources were monitored. Results Newly diagnosed breast cancer patients from 2015-2019 were compared to the pre-protocol era (2015-2017). Time from first breast clinic visit to oncology appointment decreased 39 days (60% decrease, median of 63.0 vs 22.5 days, p<0.001), no patients ≥65 years with stage I hormone-positive breast cancer in 2018-2019 received radiation therapy, and rates of ordering of CT, PET, and bone scans for asymptomatic patients decreased by 80%. Overall survival did not differ by cohort protocol category/treatment choices (p=0.69) Compared to the pre-protocol cohort, the post-protocol cohort did not have a significantly lower risk of death (Hazard Ratio 0.66, 95% Confidence Interval 0.08-5.38, p=0.69). Overall breast cancer care cost decreased by $3,675,374 between 2018 and 2019 versus 2015 to 2017. Conclusions After initiating a breast cancer program focused on reducing rates of use of four commonly excessively ordered breast cancer resources our team identified as lower-value, care at our safety-net hospital achieved high compliance with NCCN maging guidelines and also reduced use of a low-value diagnostic test, and low-value radiation and chemotherapy.
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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 | 0.000 | 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".