Cost-effectiveness Analysis of Abdominal-based Autogenous Tissue and Tissue-expander Implant following Mastectomy
Bibliographic record
Abstract
BACKGROUND: Patients who had undergone both autologous abdominal tissue (AAT) and tissue expander and implant (TE/I) breast reconstruction reported satisfaction with their reconstruction. While aesthetics and quality of life are important, the cost associated with these procedures must also be considered when choosing one method over the other. The objective of this study was to determine whether AAT-based breast reconstruction is cost-effective compared with 2-stage TE/I reconstruction at a 12-month follow-up. METHODS: Thirty-five patients consented and complied to participate in the study with a follow-up of 12 months. The effectiveness of both AAT and TE/I was measured using the Health Utilities Index Mark 3 (HUI-3). From the HUI-3 results, quality-adjusted life years were calculated for each reconstructive approach. Direct healthcare and productivity costs were captured from surgeon billing codes, patient files, and patient diaries. The perspectives of both the Ministry of Health and of society were considered. RESULTS: From the perspectives of both the Ministry of Health and of society, AAT was less effective and more costly when compared with TE/I. CONCLUSIONS: In this economic evaluation, TE/I dominated AAT, in that TE/I was more effective and less costly as compared with AAT from the perspectives of both the Ministry of Health and of society at 12 months of follow-up. This conclusion should be interpreted with caution due to a small sample size, the short timespan of the study, and the nonrandomized study design.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".