Antiemetic recommendations for breast cancer patients receiving highly emetogenic chemotherapy: A systematic review incorporating network meta-analyses.
Bibliographic record
Abstract
e17608 Background: Despite consensus group recommendations for anti-emetic use in patients receiving highly emetogenic anthracycline and cyclophosphamide-based chemotherapy (A and C-CT), clinical experience suggests that control of chemotherapy-induced nausea and vomiting [CINV] is suboptimal. Network meta-analysis (NMA) of randomised controlled trials (RCTs) was used to compare the effectiveness of competing anti-emetic regimens. Methods: A systematic literature search of 3 electronic databases for RCTs comparing anti-emetic regimens in breast cancer patients receiving A and C-CT was performed. Two reviewers independently screened all abstracts and full texts. The primary outcome was overall total control of CINV (no nausea, no vomiting, and no rescue anti-emetics for 5 days post-chemotherapy [CT]). Results: From 1,062 citations identified, 152 were retained after abstract screening, and 30 were retained after full-text screening. Most comparisons in the network of treatments were supported by only one RCT. Limitations to performing a network meta-analysis included significant heterogeneity in the number of anti-emetic regimens (n=15), chemotherapy regimen used, and mixed populations of tumour types across trials. We found over 15 different published study endpoints that were variations of our primary outcome. Of the 30 trials that met our inclusion criteria, 6 reported overall total control and 15 reported overall complete response (no vomiting and no rescue medications for 5 days post-CT). Results from NMA failed to identify important differences between competing regimens. Conclusions: We identified marked heterogeneity between trials including variability in study design, sample size, anti-emetic regimens, chemotherapy regimens, and reporting of patient characteristics and outcome measures. Given these limitations, despite the recommendations of consensus groups, NMA was unable to identify an optimal anti-emetic regimen based on all the available evidence at this time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".