Evaluating Rates of Preoperative Medical Optimization to Correct Anemia in Patients Undergoing Myomectomy
Bibliographic record
Abstract
Objective: The aims of this retrospective cohort study were to determine the proportion of women on medical therapy to correct anemia, defined as hemoglobin <12.0g/dL prior to myomectomy and to determine the association between preoperative optimization and transfusion rates, accounting for preoperative anemia. Materials and Methods: Patients undergoing myomectomy (open, laparoscopic, or robot-assisted) between February 2015 and June 2018 at a single high-volume academic hospital were included. Results: There were 224 patients who underwent open (70.5%), laparoscopic (10.7%), or robotic (18.8%) myomectomy, with 30.4% ( n = 68) anemic immediately prior to surgery. Of those patients, 76.5% ( n = 52) received medical preoperative optimization before surgery: 23 (33.8%) had iron therapy alone; 16 (23.5%) had hormonal therapy alone; 12 (17.7%) had iron and hormonal therapy; and 9 (13%) had tranexamic acid. Perioperative blood transfusion—a transfusion given intraoperatively or within 2 days postoperatively was given to 32 (14.3%) patients; 84.4% ( n = 27) were open cases. Half ( n = 16) of the transfused patients were anemic before surgery and 25% were not receiving preoperative medical optimization. Preoperative anemia significantly increased the odds of perioperative blood transfusion (odds ratio [ OR ] = 2.69, 95% confidence interval [CI] :1.26–5.77; p = 0.011). Taking medications prior to surgery did not affect the odds of receiving transfusion across all patients, including those with preoperative anemia (adjusted OR = 0.87; 95% CI: 0.38–1.98; p = 0.732). Conclusions: One quarter of transfused patients were not on medications preoperatively despite being anemic. An attempt should be made to optimize and correct anemia actively prior to myomectomy, particularly for a planned open procedure. (J GYNECOL SURG 38:120)
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".