Canadian Society of Plastic Surgeons / Société Canadienne Des Chirurgiens Plasticiens77 <sup>th</sup> Annual Meeting / 77e Réunion annuelle June 18-22 juin 2024 Halifax, NS/N-É
Bibliographic record
Abstract
PURPOSE: Periprosthetic infection (PPI) is a rare complication associated with alloplastic breast reconstruction resulting in reconstructive failure, delay of adjuvant therapies, and re-operations.Despite multiple observational cohort studies, the optimal PPI management remains unclear.The aim of this study was to develop a clinical prediction tool to guide management of PPI.METHODS: A multicenter retrospective cohort study was conducted.Consecutive breast cancer patients who underwent immediate alloplastic breast reconstruction between 2010-2020 were included.Data was collected including patient, oncologic, and reconstructive factors for patients whose postoperative course was complicated by either cellulitic or periprosthetic infection.Two models were created for prediction of progression from cellulitis to PPI and from breast infection to reconstructive failure.RESULTS: A total of 1468 patients (2165 breasts) were included.The incidence of infection was 7.1% (n = 145).The implant reconstruction was salvaged in 67.1% (n = 104) of cases.The first model, predicting progression from cellulitis to periprosthetic infection, had good predictive accuracy with an AUC of 0.61 (95% CI 0.53-0.70;p < 0.001).The second model, predicting progression to recons1e failure had good predictive accuracy with an AUC of 0.79 (95% CI 0.71-0.87;p < 0.001).Models were converted into risk stratification tools where five clinical variables were identified for a prediction scoring model with weighted points for each tool.CONCLUSIONS: This study presents novel clinical prediction tools with good predictive accuracy.Application of these prediction tools can assist the clinician in making evidence-based treatment decisions for treatment of PPI following alloplastic breast reconstruction.Future research will aim to prospectively validate treatment algorithms and improve reconstructive success. LEARNING OBJECTIVES:To identify prognostic factors in the progression of breast cellulitis to PPI and reconstructive success following PPI.To discuss how these prognostic factors can be applied to guide clinical management of breast infection following alloplastic breast reconstruction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".