Death Anxiety in Patients with Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Context: Death anxiety (DA) is a common experience in many patients with breast cancer (BC) that affects their quality of life. Objectives: The aim of this study was to estimate the mean of DA in patients with BC using a systematic review and meta-analysis approach. Data Sources: An extensive search was conducted in English electronic databases, including PubMed, Web of Science (WOS), Scopus, and Embase, up to the end of December 2024. The search aimed to gather all studies reporting the mean of DA in patients with BC, ensuring a comprehensive collection of relevant literature without time limitations. Study Selection: The inclusion criteria focused on studies that reported the mean of DA in patients with BC and were published in English. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS), ensuring that only high-quality studies were considered for the meta-analysis. Data Extraction: Data were meticulously extracted from the selected studies, emphasizing the mean of DA and other pertinent metrics. A meta-analysis was then performed using the random-effects DerSimonian-Laird model, and publication bias was evaluated using a funnel plot and Begg's and Egger's tests. Results: A total of 789 studies were found in the extensive search of electronic databases. Nine articles were selected based on the inclusion and exclusion criteria. Four studies were conducted in Iran, two in China, and one in Pakistan, Turkey, and Palestine. All of the included studies were cross-sectional and published between 2017 and 2024. In total, this meta-analysis included 6 articles. The pooled mean of DA in BC patients was 9.96 [confidence interval (CI): 7.99 - 11.93, I2 = 99.69, P < 0.001]. Conclusions: This study showed that the mean of DA is high among BC patients. Therefore, it is recommended to use supportive and psychological measures in comprehensive oncology care to control this challenge.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".