Current Practice Among Plastic Surgeons of Antibiotic Prophylaxis and Closed-Suction Drains in Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: Despite their widespread use, there are no evidence-based guidelines on the management of closed-suction drains or antibiotics in postmastectomy breast reconstruction. The purpose of this study was to assess consensus and variation in postoperative care among plastic surgeons. METHODS: The authors designed and administered a self-reported, anonymous survey to 4669 American Society of Plastic Surgeons and Canadian Society of Plastic Surgeons members in October 2009. RESULTS: A total of 650 completed surveys were available for analysis. A majority (>81%) of respondents reported using closed-suction drains in breast reconstruction. Most surgeons (>93%) used a volume criteria for drain removal, most commonly when drain output was ≤30 mL over 24 hours (>86%). Preoperative antibiotic use was nearly universal (98%), usually consisting of intravenous cefazolin (97%). Postoperative care demonstrated less uniformity with outpatient antibiotics administered by 72% of respondents. Surgeons were divided on when to discontinue outpatient antibiotics: 46% preferred concomitant discontinuation with drains, whereas 52% preferred a specific postoperative day. No clear consensus was observed for the number (1 or 2) or type (Jackson-Pratt or Blake) of drains used. Respondents were further divided on the restriction of postoperative showering with drains and the use of acellular dermal matrix. CONCLUSIONS: These results demonstrate a consensus for drain use, drain removal, and preoperative antibiotic administration. There was no consensus for number or type of drain used, postoperative antibiotic use, shower restrictions, and use of acellular dermal matrix. Our results further emphasize the need for evidence-based postoperative-care guidelines specific to breast reconstruction.
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".