Prophylaxis and treatment of cancer-related dyspnea with pharmacologic agents: A systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cancer-related dyspnea is a common symptom in patients with cancer. It has also been reported to be a predictor of poorer prognosis, which can then change clinical treatment and advance care planning. Currently, no definitive recommendation for pharmacologic agents for cancer-related dyspnea exists. The aim of this systematic review and network meta-analysis is to compare pharmacologic agents for the prophylaxis and treatment of cancer-related dyspnea. METHODS: A search was conducted in the databases of PubMed, Embase, and Cochrane CENTRAL through May 2021. Standardized mean differences (SMDs), as reported by studies or calculated from baseline and follow-up dyspnea scores, were amalgamated into a summary SMD and 95% confidence interval (CI) using a restricted maximum likelihood multivariate network meta-analysis. RESULTS: Twelve studies were included in this review; six reported on prophylaxis of exertional dyspnea, five on treatment of everyday dyspnea, and one on treatment of episodic dyspnea. Morphine sulfate was better at controlling everyday dyspnea than placebo (SMD 1.210; 95% CI: 0.415-2.005). Heterogeneity in study design and comparisons, however, led to some concerns with the underlying consistency assumption in network meta-analysis design. CONCLUSION: Optimal pharmacologic interventions for cancer-related dyspnea could not be determined based on this analysis. Further trials are needed to report on the efficacy of pharmacologic interventions for the prophylaxis and treatment of cancer-related dyspnea.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".