Outcomes of a Multidisciplinary Team in the Management of Patients with Early-Stage Breast Cancer Undergoing Neoadjuvant Chemotherapy at a Community Cancer Center
Bibliographic record
Abstract
Background: The utilization of neoadjuvant chemotherapy (NAC) remains highly variable in clinical practice. The implementation of NAC requires coordination of handoffs between a multidisciplinary team (MDT). This study aims to assess the outcomes of an MDT in the management of early-stage breast cancer patients undergoing neoadjuvant chemotherapy at a community cancer center. Methods: We conducted a retrospective case series on patients receiving NAC for early-stage operable or locally advanced breast cancer coordinated by an MDT. Outcomes of interest included the rate of downstaging of cancer in the breast and axilla, time from biopsy to NAC, time from completion of NAC to surgery, and time from surgery to radiation therapy (RT). Results: Ninety-four patients underwent NAC; 84% were White and mean age was 56.5 yrs. Of them, 87 (92.5%) had clinical stage II or III cancer, and 43 (45.8%) had positive lymph nodes. Thirty-nine patients (42.9%) were triple negative, 28 (30.8%) were human epidermal growth factor receptor (HER-2)+, and 24 (26.2%) were estrogen receptor (ER) +HER-2−. Of 91 patients, 23 (25.3%) achieved pCR; 84 patients (91.4%) had downstaging of the breast tumor, and 30 (33%) had axillary downstaging. The median time from diagnosis to NAC was 37.5 days, the time from completion of NAC to surgery was 29 days, and the time from surgery to RT was 49.5 days. Conclusions: Our MDT provided timely, coordinated, and consistent care for patients with early-stage breast cancer undergoing NAC as evidenced by time to treatment outcomes consistent with recommended national trends.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".