Predictive Patient Characteristics and Surgical Variables That Influence Postoperative Complications following Bilateral Reduction Mammoplasty
Bibliographic record
Abstract
Background: Breast hypertrophy is known to be a source of both physical and psychosocial health deficits. Therefore, the ability to relieve these symptoms with surgical treatment is an important consideration for patients. The primary objective of this study was to assess the impact of patient body mass index (BMI) on postoperation complications. The secondary objective of this study was to assess patient demographics, surgical techniques, and patient comorbidities for their impact on specific postoperative complications. Methods: A retrospective chart review of all patients who received bilateral breast reduction surgery in Nova Scotia over the past 10 years was performed. A total of 1022 patients met the inclusion criteria of the study. Logistic regression modeling was performed to identify demographic factors, surgical techniques, and patient comorbidities that impact the risk of developing specific postoperative complications. Results: Our study population had a total complication incidence of 37.7%. BMI was not significantly different between patients who developed complications and those who did not. Logistic regression modeling showed a significant relationship that with each unit increase in BMI above the mean (25.9 kg/m2) the relative risk of patient-reported postoperative asymmetry increased by 6%. Conclusions: The findings of this study suggest that BMI has several nonsignificant relationships to postoperative complications following bilateral breast reduction. These trends do not translate to significantly increased complaints of asymmetry, scarring‚ or revision surgeries. This study also provides valuable information on the timeline of postoperative complications and when they can commonly be identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".