Autologous Blood Transfusion in TRAM Breast Reconstruction:
Bibliographic record
Abstract
In Brief Many centers continue to use preoperative donation of autologous blood as part of their reconstructive protocol for pedicled transverse rectus abdominis musculocutaneous (TRAM) breast reconstruction, despite the lack of support for this in the English language literature. This prospective study compares 3 groups of patients undergoing reconstruction with TRAM flaps using 3 different protocols in 3 different centers. Group 1 did not donate blood preoperatively. Group 2 donated 1 to 2 U preoperatively and received their blood intraoperatively or during the early postoperative period. Group 3 did not receive their autologous blood unless they displayed symptoms of hypovolemia or anemia postoperatively. There were no statistical differences between groups in age, length of stay, or number of unilateral versus bilateral procedures. Patients who did not donate autologous blood (group 1) had statistically significantly higher preoperative and postoperative day 3 hemoglobin levels than patients in the groups that did predonate. The authors conclude that preoperative autologous donation of blood does not confer any clinical advantage to patients undergoing autologous breast reconstruction using pedicled TRAM flaps. Patients undergoing pedicled TRAM reconstruction followed 3 protocols regarding autologous blood donation: (1) No donation, (2) Preoperative donation and intraoperative/postoperative autologous transfusion, or (3) Preoperative donation but no transfusion unless hypovolemic or anemic. Patients who did not donate had higher preoperative and 3-day postoperative hemoglobins than either other group.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".