The Prevalence and Factors of Dyspnea Among Advanced Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Dyspnea is a significant symptom in advanced cancer patients, yet comprehensive evidence on its prevalence and related factors is lacking. OBJECTIVE: This review aims to summarize the prevalence of dyspnea among advanced cancer survivors and identify associated factors. METHODS: MEDLINE, EMBASE, Cochrane Library, PsycINFO, CINAHL Plus, and Web of Science were searched from inception to May 2024. Observational studies focusing on advanced cancer patients reporting dyspnea were included. Two reviewers performed data extraction and quality assessment independently using the Newcastle-Ottawa Scale. Prevalence estimates were pooled using a random-effects model. Subgroup analyses and metaregression were performed to explore heterogeneity. RESULTS: A total of 67 studies involving 78 409 advanced cancer survivors were included, revealing a pooled prevalence of dyspnea of 43% (95% prediction interval, 0.07, 0.84). Significant variations were observed based on cancer types, with lung cancer showing a prevalence of 55%. Factors associated with dyspnea were categorized using the Breathing, Thinking, Functioning clinical model: (1) breathing: physical (eg, fatigue), medical (eg, lung disease), and treatment-related (eg, palliative sedation) factors; (2) thinking: psychological (eg, anxiety) factors; and (3) functioning: performance (eg, Karnofsky Performance Status) and demographic characteristics (eg, age). CONCLUSIONS: The findings highlight a high prevalence of dyspnea among advanced cancer survivors and identify several associated factors, stressing the need for early detection and comprehensive management strategies. IMPLICATIONS FOR PRACTICE: Health providers can improve the quality of life for patients by effectively managing dyspnea, thereby reducing symptom burden, and alleviating psychological distress, leading to better overall well-being for patients and caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".