Commentary: Trends and Early Complications in Direct-to-Implant Breast Reconstruction: An Updated Analysis of the ACS-NSQIP Database
Bibliographic record
Abstract
It is a pleasure to provide a brief commentary on the article "Trends and Early Complications in Direct-to-Implant Breast Reconstruction: An Updated Analysis of the ACS-NSQIP Database" by Plotsker et al. 1 The authors are an accomplished group from a very busy cancer center with extensive experience in post-mastectomy breast reconstruction.The two primary goals of this article were to analyse early (30 day) complication rates following immediate direct-to-implant (DTI) breast reconstruction and to assess the change in DTI rates in mastectomy patients over a ten-year period from 2010 to 2019.The authors chose to group early complications into two main categories: major surgical complications and medical complications.Data was obtained from the American College of Surgeons -National Surgical Quality Improvement Program (ACS-NSQIP) and was compared to a previously published study by Wink et al 2 which analysed similar data between 2005 and 2010.The primary findings were a major surgical 30-day complication rate of 10%, a medical 30-day complication rate of 0.83%, and a trend in DTI reconstruction rates from 5.2% in 2010 to 15.1% in 2019.The authors point out that the early complication rate was similar to that reported by Wink from 2005 to 2010 (9%).Specific risk factors linked to complications were elevated BMI, history of smoking, hypertension, blood disorders, and intraoperative blood transfusion.With alloplastic breast reconstruction being the predominant option for postmastectomy breast reconstruction, along with the increasing trend towards DTI procedures, this updated data from a comprehensive national database is important and welcome information to be used during patient selection as well as informed consent.Of note, the major surgical complication rate was largely stable between 9% and 10% comparing data over the course of 15 years.One is left to wonder why advances such as improved attention to mastectomy technique, intraoperative assessment of tissue perfusion, and increasing use of acellular dermal matrices, has not positively impacted this rate.Perhaps those advances have been offset by other changes such as an increased percentage of nipple-sparing mastectomies or greater usage of the prepectoral plane.Further research will be necessary to tease out the impact of these changing trends.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".