A Systematic Review of Comparison of Autologous, Allogeneic, and Synthetic Augmentation Grafts in Nipple Reconstruction
Bibliographic record
Abstract
BACKGROUND: Many techniques have been described for nipple reconstruction, with the principal limitation being excessive loss of projection. The ideal reconstructed nipple provides sustained projection, the fewest complications, and high levels of patient satisfaction. A variety of materials are available for projection augmentation, including autologous, allogeneic, and synthetic materials. To date, there has been no systematic review to study the efficacy, projection, and complication rates of different materials used in nipple reconstruction. METHODS: MEDLINE, Embase, and PubMed databases were searched, from inception to August of 2014, to identify literature reporting on outcomes of autologous, allogeneic, and synthetic grafts in nipple reconstruction. Retrospective and prospective studies with controlled and uncontrolled conditions were included. Studies reporting the use of autologous flap techniques without grafts and articles lacking postoperative outcomes were excluded. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Thirty-one studies met the inclusion criteria. After evidence review, one study represented two of nine stars on the Newcastle-Ottawa Scale, two studies represented three stars, six studies represented four stars, seven studies represented five stars, 11 studies represented six stars, and four studies represented seven stars. CONCLUSIONS: The results of this review revealed heterogeneity in the type of material used within each category and inconsistent methodology used in outcomes assessment in nipple reconstruction. Overall, the quality of evidence is low. Synthetic materials have higher complication rates and allogeneic grafts have nipple projection comparable to that of autologous grafts. Further investigation with high-level evidence is necessary to determine the optimal material for nipple reconstruction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".