Outcomes and Economic Evaluation in Delayed Two-Stage Breast Reconstruction in Romania: The Influence of Radiotherapy
Bibliographic record
Abstract
The current paper is a retrospective cohort study conducted on sixty-seven patients who underwent two-stage breast reconstruction over a 5-year period (2015–2020). Forty-one (61.2%) patients received radiotherapy (RT group), and twenty-six (38.8%) did not (non-RT group). Data regarding patients, oncological therapies, type of reconstruction, time of hospitalization, complications, and costs were collected. The statistical analysis was performed using IBM SPSS Statistics 25. General complications were noted for 18 patients (43.9%) in the RT group and for 7 patients (26.9%) in the non-RT group. Major complications were observed only in the first group (five patients–12.2%). The mean time of hospitalization in the RT group was 14.83 days for patients with complications versus 9.83 days for those without complications and 15.5 days versus 8.63 days, respectively, in the non-RT group. The mean cost for patients without complications was 235.64 euros, whereas the cost for patients with complications was 330.24 euros (p = 0.001). Radiation therapy can affect the overall outcome by increasing the risk of complications and increasing costs; however, our paper shows that the association of alloplastic reconstruction in patients with radiotherapy can be performed safely and with low costs in carefully selected patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".