Twelve-year delay of a nipple-sharing graft: A case report.
Bibliographic record
Abstract
S urgical treatment of breast cancer has evolved from radical mastec- tomy with removal of the nipple areolar complex (NAC), to breast conservation therapy with preservation of the breast and NAC.Increasing interest in improved cosmesis has led to the introduction of the skin-sparing and nipple-sparing mastectomy as potential alternatives to complete mastectomy (1).There continue to be clinical situations in which the NAC is removed to either treat disease or as a component of breast cancer risk reduction.Several techniques are available to reconstruct the NAC.Achievement of consistent quality results remains a challenge.Often, multiple surgical procedures are required to achieve an acceptable cosmetic outcome.Jabor et al (2) reported a high level of dissatisfaction with NAC reconstructions, with only 16% of patients stating they had no desire to change their reconstruction.The remaining patients reported, in decreasing order, dissatisfaction with the nipple projection, colour match, shape, size, texture and position of their reconstructed NAC.Holding to the principle of reconstructing like tissue with like tissue, the nipple-sharing composite graft is an ideal approach for unilateral reconstruction.Requirements for this technique include unilateral reconstruction, a donor nipple of adequate size and patient consent for partial removal of the remaining normal nipple.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".