Temporal Sequencing of Multimodal Treatment in Immediate Breast Reconstruction and Implications for Wait Times: A Regional Canadian Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Treatment of breast cancer requires a multimodal approach with numerous independent specialists. Immediate breast reconstruction (IBR) adds another layer of coordination to comprehensive breast cancer care. To optimize health outcomes for patients seeking IBR, it is essential to efficiently coordinate the temporal sequence of care modalities inclusive of reconstruction. Methods: In this cross-sectional study, patients undergoing IBR following complete or partial mastectomy at one health centre from 2015 to 2021 were included. Patients were categorized into two main groups defined by the first treatment modality received, namely surgery first and Neoadjuvant Chemotherapy. Primary outcome measures were wait times for diagnostic investigations, initiation of treatment, and transitions between therapeutic modalities. Results: Of 195 patients, 158 underwent surgery first, and 37 underwent neoadjuvant chemotherapy. Median wait time from first consultation to first treatment initiated in the neoadjuvant cohort was shorter by 11.5 days as compared to the Surgery First cohort (21.5 +/− 19 vs 33.0 +/− 28 days; P = 0.001). Twenty-three (82%) of the surgery first and 11 (38%) of the neoadjuvant cohort patients waited longer than 8 weeks for initiation of radiotherapy ( P = 0.001). Following surgical intervention, the majority of patients failed to meet target benchmarks for transition to chemotherapy ( n = 25, 53%) and transition to radiotherapy ( n = 26, 93%; P < 0.001). Conclusion: Patients undergoing IBR may incur delays in the setting of upfront surgery and in transitioning to adjuvant therapies. In the setting of breast reconstruction, further efforts are required to achieve target wait-times in multimodal breast cancer care.
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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 | 0.001 | 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".