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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".