The incidence of perioperative anemia and iron deficiency in patients undergoing gyne-oncology surgery
Bibliographic record
Abstract
Preoperative anemia is progressively being recognized as a risk factor for poor perioperative outcomes including increased length of hospital stay and increased blood transfusions. The growth in prevalence of preoperative anemia in patients undergoing gynecological oncology procedures warrants greater attention to early identification for optimal surgical outcomes. This was a quantitative retrospective observational study consisting of 284 patients undergoing gynecological oncology procedures. The study sought to determine the frequency of anemia, iron deficiency and the effect of anemia on the number of blood transfusions from January 1 to December 31, 2014. Patients with anemia had significantly higher transfusion rates (44% versus 11%, p < 0.0001), considerably higher number of units transfused per patient (mean 1.19 units versus 0.28 units, p < 0.0001) and longer length of stays post-operatively (mean 5.9 days versus 4.6 days, p=0.0008). It was concluded that early identification and treatment of anemia is a key opportunity to optimized surgical outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".