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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".