One Hundred Forty-One Consecutive Attempts at Autologous Tissue Single-Stage Breast Cancer Reconstruction
Bibliographic record
Abstract
The analysis of a single surgeon's 5 year attempt at performing autologous tissue breast cancer reconstruction, with one general anesthetic. A single-stage breast cancer reconstruction is successful if after the original reconstruction, no correction for complications, revision of breast mound, or contralateral breast procedures are performed, under general anesthetic, to complete the reconstruction. This is a review of a single surgeon's breast reconstruction practice. Three hundred fifty-six breast cancer reconstruction patients had surgery in a period of 5 years. One hundred forty-one of 356 (39.6%) were consecutive attempts at single-stage autologous tissue reconstruction: 106 of 141 (75.1%) were free abdominal flaps (transverse rectus abdominis myocutaneous, muscle-sparing flaps [MS], or deep inferior epigastric artery perforator flaps), 29 of 141 (20.6%) pedicled transverse rectus abdominis myocutaneous flaps, and 6 of 141 (4.3%) latissimus dorsi flaps; 37 of 141 (26.2%) were immediate reconstructions, 100 of 141 (70.9%) delayed reconstructions, and 4 of 141 (2.8%) mixed reconstructions. One hundred seven of the 141 patients (75.9%) had their autologous tissue reconstruction successfully performed in one general anesthetic. Reconstructions requiring more than one general anesthetic were due to 18 of 141 (12.8%) postoperative and donor-site complications, 16 of 141 (11.3%) revisions of breast mound or contralateral breast procedures. A total of 34 of 141 (24.1%) reconstructions required a second general anesthetic for successful completion, only 16 of 141 (11.3%) of autologous tissue breast cancer reconstructions required revisions for symmetry. Therefore, single-stage breast cancer reconstruction is feasible and should be attempted to decrease the morbidity of breast cancer survivors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".