Efficacy of Different Interventions to Reduce Pre- or Perioperative Blood Transfusion Rate in Patients with Colorectal Cancer: A Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: The high proportion of blood transfusions before and during surgery carries unnecessary risk and results in poor prognosis in colorectal cancer patients. Different pharmacological interventions (i.e., iron supplement or recombinant erythropoietin) to reduce blood transfusion rates have shown inconclusive results. Methods: This network meta-analysis (NMA) consisted of randomized controlled trials (RCTs) comparing the efficacy of different pharmacologic interventions (i.e., iron supplementation or recombinant erythropoietin) to reduce the blood transfusion rate. NMA statistics were conducted using the frequentist model. Results: Seven RCTs (688 participants) were included in this study. The NMA demonstrated that the combination of high-dose recombinant human erythropoietin and oral iron supplements was associated with the least probability of receiving a blood transfusion [odds ratio = 0.24, 95% confidence intervals (95% CIs): 0.08 to 0.73] and best reduced the amount of blood transfused if blood transfusion was necessary (mean difference = −2.62 U, 95% CI: −3.55 to −1.70 U) when compared to the placebo/control group. None of the investigated interventions were associated with any significantly different dropout rate compared to the placebo/control group. Conclusions: The combination of high-dose recombinant human erythropoietin and oral iron supplements might be considered as a choice for reducing the rate of blood transfusion in patients with colorectal cancer. However, future large-scale RCT with long-term follow-up should be warranted to approve the long-term safety.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".