Surgical Indication and Approach are Associated with Transfusion in Hysterectomy for Benign Disease
Bibliographic record
Abstract
Objective: To identify pre-operative and intraoperative factors associated with the risk of red blood cell transfusion among women undergoing hysterectomy. Methods: A retrospective cohort study of hysterectomy for benign indications between January 1, 2011 - December 31, 2017. Patients receiving blood transfusion within 30 days of surgery were compared to patients who did not receive any transfusion. Multivariate logistic regression analysis was performed to identify clinical and surgical variables associated with blood transfusion. Results: Among 171,940 women who underwent hysterectomy for benign indication, 4,667 (2.7%) required blood transfusion. The rate of transfusion was highest among patients with uterine fibroids (4.3%) and lowest in patients with genital prolapse (1.1%) (p < 0.05). Odds of blood transfusion were significantly elevated in patients undergoing hysterectomy for uterine fibroids compared to patients with genital prolapse (adjusted odds ratio [aOR] 1.36, 95% confidence interval [CI] 1.15 - 1.61). Other patient characteristics included body mass index, smoking, bleeding disorders, pre-operative sepsis, and American Society of Anesthesiologists score ≥ 2 (p < 0.05). Higher pre-operative hematocrit significantly decreased the risk of blood transfusion (aOR 0.84, 95% CI 0.84 - 0.85 per percent increase in hematocrit). Abdominal and vaginal hysterectomies were associated with greater odds of transfusion compared with laparoscopic approaches (aOR 5.06, 95% CI 4.70 - 5.44; aOR 1.87, 95% CI 1.67 - 2.10, respectively). Conclusions: Certain patient comorbidities, surgical indication, and approach to hysterectomy are associated with increased risk of blood transfusion. These results may have implications for pre-operative patient counseling, perioperative care, and health system planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".