The Sequence and Reconstructive Modality of Breast Cancer Treatments Affects Wait Times to Adjunctive Therapies in Patients Undergoing Mastectomy with Immediate Breast Reconstruction
Bibliographic record
Abstract
Introduction: Breast cancer care requires both oncologists and plastic surgeons. Coordinating these specialists and combining extirpative and reconstructive procedures before adjunctive therapies can cause delays in care. For patients with less advanced disease, surgery is performed before adjunctive therapies, requiring early specialist coordination and the possibility of surgical complications. We compare these patients to those with more advanced disease requiring adjunctive therapies before surgery. Methods: A retrospective chart review identified 337 post-mastectomy + immediate breast reconstruction (IBR) patients. Patients were divided into surgery first (SF) and neoadjuvant chemotherapy (NC) first groups with reconstructive subgroups. Wait times between care pathway milestones were compiled and compared to national standards. Results: SF experienced longer wait times from consultation to treatment initiation (47 ± 51.5 vs 22 ± 22, P <.001) and from first to second treatment modality (62 ± 35 vs 39 ± 17, P <.001). Furthermore, only 29% of SF met the standard of receiving treatment within 4 weeks from consultation compared to 63% of NC ( P <.001). Within subgroups, SF alloplastic reconstructions had shorter wait times compared to SF autologous reconstructions. For SF, only 31% of alloplastic and 24% of autologous reconstruction initiated treatment within 4 weeks of consultation. Conclusion: In this cohort of Canadian breast cancer patients, those receiving surgery first experienced prolonged wait times to treatment, particularly with autologous reconstruction. Patients should be informed of the potential impact on adjunctive therapies when considering reconstructive modality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".