Breast Reconstruction Use And Impact On Oncologic Outcomes Amongst Inflammatory Breast Cancer Patients: A Systematic Review
Bibliographic record
Abstract
Introduction: Women with inflammatory breast cancer (IBC) are often advised against breast reconstruction due to concerns about recurrence and poor long-term survival. Our study aims to characterize trends and predictors for breast reconstruction use, and its impact on oncologic outcomes amongst women with IBC. Methods: We systematically searched MEDLINE, Embase, and the Cochrane Library for all studies published up to February 1, 2022, using MeSH and EMTREE headings with free text combinations. Randomized controlled trials and cohort studies comparing women diagnosed with IBC undergoing a mastectomy with or without breast reconstruction were evaluated. Results: The initial search yielded a total of 203 studies, of which 8 eligible studies, all retrospective cohort studies, reporting 2,393 cases of breast reconstruction in 25,082 women with IBC were included in the final review. In the past two decades, reconstruction rates have risen from 6.3% to 12.3%, with younger age, higher income, private insurance, and living in metropolitan areas being associated with reconstruction. Four studies found no difference between women undergoing vs not undergoing reconstruction for overall survival or local recurrence rates, while two studies found improved overall survival amongst women who underwent reconstruction. Immediate breast reconstruction was associated with increased risk of postoperative complications in two studies, compared to no or delayed reconstruction. Delays in starting adjuvant therapy were not different between the groups. Conclusion: Breast reconstruction after mastectomy may be reasonable to consider for select patients with IBC who desire the procedure, as it is not associated with worse survival or increased local recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".