Intravenous Fluid Infusion Rate in Microsurgical Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the role of intravenous fluid infusion rate in the development of in-hospital complications in patients undergoing microsurgical breast reconstruction for breast cancer. METHODS: A retrospective review was performed between 2002 and 2009 at a single institution for all consecutive patients undergoing free flap reconstruction of the breast. The authors examined patient variables (age; body mass index; preoperative hemoglobin, hematocrit, and creatinine levels; American Society of Anesthesiologists classification; and cardiac risk factors), surgical variables (type of reconstruction, timing, laterality, need for blood transfusion, and duration of general anesthesia), and fluid variables (rate of crystalloid and colloid infusion in the first 24 hours standardized by weight). The primary outcome was in-hospital complications. The impact of each factor was first determined using univariate tests. The final multivariate logistic regression model was compiled based on variables found to be significant from the univariate analysis and variables felt a priori to affect complication rates. RESULTS: Of the 260 patients who had a total of 354 free flaps for breast reconstruction, 54 (20.8 percent) had postoperative complications. There were 40 surgical complications (15.4 percent) and 11 medical complications (4.2 percent), and three patients (1.2 percent) had both types. Most complications were flap related (7.3 percent), including two total flap losses (0.8 percent). Multivariate analysis suggested that the extremes of crystalloid infusion rate significantly predicted postoperative complications (p = 0.03) after adjusting for the effect of other covariates. CONCLUSION: : This is the first study to report that crystalloid infusion rate, a modifiable variable, is an important predictor of postoperative complications following microsurgical breast reconstruction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".