Periareolar minimally invasive approach for cardiac surgery: a case series and description of technique with a review of literature
Bibliographic record
Abstract
BACKGROUND: Minimally invasive cardiac surgery (MICS) has garnered significant attention for its potential benefits, including decreased surgical trauma, accelerated recovery, and improved aesthetic outcomes. This case series aims to elucidate the technical aspects and assess the aesthetic, functional, and quality of life outcomes associated with the utilization of a periareolar incision approach in female patients undergoing cardiac surgery. METHODS: The periareolar MICS technique, performed with or without high-definition (HD) 3D endoscopic visualization, limited rib-spreading, and a periareolar incision spanning the 3 to 9 o'clock positions, was employed. We present a case series encompassing five female patients who underwent various cardiac procedures for different pathologies using this approach. RESULTS: No intraoperative complications occurred, and all patients experienced uneventful postoperative recoveries. The periareolar approach resulted in well-healed incisions with minimal scaring, preserving breast contour and yielding satisfactory cosmetic outcomes. Patients reported negligible pain levels and expressed contentment with the scar appearance. CONCLUSION: The periareolar incision technique in MICS represents an efficacious approach characterized by favorable aesthetic outcomes and enhanced patient experience. Further investigations are warranted to compare different MICS approaches with respect to pain management and their impact on quality-of-life domains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".