Prevalence and risk factors for cancer-related fatigue in women with malignant gynecological tumors: a meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue (CRF) is one of the most prevalent symptoms, but its prevalence and associated risk factors remain inconsistent across studies. OBJECTIVE: To identify the prevalence and risk factors for CRF in women with malignant gynecological tumors. METHODS: A comprehensive search of databases, including Web of Science, Cochrane Library, PubMed, Embase, CNKI, VIP, Wan Fang, and CBM, was conducted for relevant studies published from the inception of the database until September 7, 2023. Two reviewers used EndnoteX9 software to independently review, extract data, cross-check, and use the Newcastle-Ottawa quality assessment scale and the Agency for Healthcare Research and Quality tool for risk of bias assessment to evaluate bias risk. Stata 17.0 software was used to perform a traditional meta-analysis. RESULTS: The meta-analysis included 33 studies, of which 29 reported the prevalence of CRF. The combined prevalence of CRF was 89% (95% confidence interval [CI]: 80-95%), and the combined prevalence of chronic CRF was 25% (95%CI: 22-28%). The combined prevalence of CRF in patients with ovarian cancer, cervical cancer, endometrial, and gynecological malignancies (including but not limited to cervical, ovarian, vaginal and other mixed types of gynecological cancers) was 77%, 94%, 90%, and 93%, respectively. The variability in CRF measurement is due to the different scales used across studies. Its prevalence varies by country, and developing countries, especially China, have a high prevalence of CRF. The following risk factors were associated with CRF: age (odds ratio [OR] = 1.43, 95%CI = 1.12-1.83), psychological factors (OR = 1.40, 95%CI = 1.14-1.72), disease stage (OR = 1.65, 95%CI 1.14-2.40), and social support (OR = 0.77, 95%CI 0.67-0.87). CONCLUSION: The prevalence of CRF is significant in women with gynecological cancers, especially in developing countries. Age, psychological factors, and disease stage are risk factors for CRF, while social support serves as a protective factor. Healthcare professionals can obtain a clearer picture of CRF in women with gynecological malignant tumors and identify risk factors to support subsequent interventions in these patients. PROSPERO ID: CRD42023489433.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.003 | 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".