Impact of chemotherapy-induced neutropenia on survival in patients with breast, ovarian and cervical cancer: a systematic review
Bibliographic record
Abstract
Background: Recent advances in chemotherapy administration, including targeted therapies and dose-dense scheduling have led to an increased incidence of neutropenia. Chemotherapy-induced neutropenia has been shown to be associated with improved treatment outcomes in various solid tumor types. We looked to summarize the relationship between chemotherapy-induced neutropenia and survival in patients with breast, ovarian and cervical cancer and describe future directions of research. Methodology/Principle Findings: A comprehensive PUBMED literature search was conducted using the key words “solid tumor,” “breast cancer,” “ovarian cancer,” “cervical cancer,” “endometrial cancer,” in combination with “chemotherapy,” “neutropenia,” “survival,” “disease recurrence,” and “prognosis.” The search was also guided through a review of the reference lists of original and review articles. Published papers included in the review met the following criteria: patients with breast, ovarian, endometrial or cervical cancer treated with chemotherapy; prospective and retrospective studies, both randomized and cohort designs, evaluating impact of neutropenia or leukopenia on disease outcome. Eleven studies met the inclusion criteria. Sample size ranged from 103-750 subjects. The majority of included studies were conducted in breast cancer patients (7 of 11). No studies related to endometrial cancer were identified. Outcome measures included overall survival, progression free survival and distant disease free survival. All studies were retrospective in nature. Of the 11 identified studies, 9 suggested that the occurrence of chemotherapy-induced neutropenia was associated with improvement in oncologic outcome. Conclusions/Significance: Evaluation of the impact of chemotherapy-induced neutropenia on outcomes in patients with breast, ovarian and cervical cancer is warranted utilizing prospectively collected data from patients enrolled in clinical trials.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".