Preoperative Anemia and Iron Deficiency in Elective Gastrointestinal Cancer Surgery Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Preoperative anemia can impact postoperative outcomes, but its importance in gastrointestinal cancer patients, and significance of anemia etiology remains unclear. We aimed to characterize the frequency and impact of preoperative anemia, and iron-deficiency anemia (IDA), on perioperative outcomes. METHODS: We performed a retrospective cohort study of adult patients undergoing elective gastrointestinal cancer surgery. The primary outcome was the incidence of perioperative RBC transfusion. Secondary outcomes included 90-day postoperative major morbidity, ICU admission, and 90-day hospital readmission. Multivariable analyses were performed to assess the association between preoperative anemia and IDA and outcomes. RESULTS: Preoperative anemia was present in 55.5% of patients (n = 15 414), and 58.3% of anemic patients were iron deficient. Preoperative anemia was independently associated with increased risk of RBC transfusion (RR 2.88, 95% CI 2.60-3.20), and secondary outcomes. For every preoperative hemoglobin decrease of 1 g/dL, the adjusted risk of perioperative RBC transfusion increased by 40% (RR 1.39, 95% CI 1.37-1.42). CONCLUSION: Preoperative anemia is prevalent, and an independent risk factor for adverse postoperative outcomes. Decreases in preoperative hemoglobin levels elevate the risk of transfusion and adverse outcomes, supporting further study to optimize management of treatable causes of preoperative anemia including IDA.
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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 | 0.003 |
| 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 | 0.000 | 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 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".