454. EFFICACY OF PEGFILGRASTIM IN PREVENTING FEBRILE NEUTROPENIA DURING DCF CHEMOTHERAPY FOR ESOPHAGEAL CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Esophageal cancer remains a leading cause of cancer-related mortality worldwide, with docetaxel, cisplatin, and fluorouracil (DCF) chemotherapy being a standard treatment option for locally advanced disease. However, DCF is associated with a high incidence of febrile neutropenia (FN), a serious complication that can lead to treatment delays, hospitalizations, and increased morbidity. Pegfilgrastim, a long-acting granulocyte colony-stimulating factor (G-CSF), is recommended for FN prevention, but its efficacy in esophageal cancer patients receiving DCF chemotherapy remains unclear. Methods A systematic literature search of PubMed, Cochrane Library, Scopus, and Web of Science identified eligible studies. Retrospective and prospective studies comparing prophylactic pegfilgrastim with placebo or no G-CSF in patients undergoing DCF therapy were included. Eight reviewers independently screened studies, extracted data, and assessed quality using the Newcastle–Ottawa Scale. The extracted data were then verified by two additional reviewers. A meta-analysis was conducted to calculate pooled effect sizes. Results Pooled analysis showed that pegfilgrastim significantly reduced FN incidence (OR = 0.283, 95% CI: 0.102–0.782, p = 0.015). FN rates ranged from 3.3% to 30.8% in the pegfilgrastim group versus 26.7% to 60.6% in the control group. Pegfilgrastim was also associated with lower rates of severe neutropenia and shorter hospital stays, with no significant increase in adverse events. Conclusion In our study, Pegfilgrastim prophylaxis significantly reduces FN risk, severe neutropenia, and hospital stays in esophageal cancer patients receiving DCF chemotherapy, without increasing major complications. These findings support its routine use in this population, though further prospective randomized trials are needed to optimize dosing strategies and confirmlong-term benefits.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".