Real-World Clinical Outcomes of Trilaciclib for the Prevention of Myelosuppression in Patients with Esophageal Cancer Undergoing Chemotherapy
Bibliographic record
Abstract
This study aims to evaluate the clinical effectiveness of trilaciclib in preventing myelosuppression in patients with esophageal cancer undergoing chemotherapy. Based on the use of trilaciclib, 81 patients were divided into a primary prevention group (PP group, n = 49) and a secondary prevention group (SP group, n = 32). The incidence of myelosuppression, antibiotic usage rate, survival outcomes, and other treatment-related toxicities were analyzed using chi-square tests and Kaplan–Meier survival curves. The incidence of chemotherapy-induced myelosuppression in the SP group was significantly higher than that in the PP group (96.9% vs. 79.6%), with a significantly higher proportion of grade III and above events (37.6% vs. 8.2%, p < 0.05). For chemotherapy-induced neutropenia, the incidence of grade III/IV events in the SP group was significantly higher than in the PP group (28.1% vs. 8.2%, p = 0.017). Additionally, the SP group experienced higher rates and severity of chemotherapy-induced anemia and thrombocytopenia. The PP group provided better protection against grade III/IV leukopenia and neutropenia (p < 0.05). Non-hematological toxicities and efficacy outcomes were similar between groups (p > 0.05). The study is the first to demonstrate that trilaciclib is a safe and effective option for the prevention of myelosuppression in esophageal cancer patients.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".