Health-related quality of life in breast cancer patients in low-and-middle-income countries in Asia: a systematic review
Bibliographic record
Abstract
Introduction: Breast cancer remains one of the major cancers worldwide. In Asia, breast cancer is leading both incidence and mortality rates. Health-related quality of life (HRQoL) studies play an important role in clinical treatment. This systematic review aimed to summarize the evidence of HRQoL and associated factors among patients with breast cancer in low-and-middle-income countries (LMICs) in Asia. Method: Performed according to PRISMA guidelines for systematic review, the studies were searched from three databases (PubMed, Cochrane, Scopus) up to November 2020. The studies which met the predefined eligibility criteria were selected, extracted, and assessed the quality according to the Newcastle-Ottawa Scale (NOS) tool. Results and Discussion: A total of 2,620 studies were searched on the three databases, of which 28 met the selection criteria, then, were included in the systematic review. The Global Health Status (GHS) score of breast cancer patients based on the EORTC QLQ-C30 questionnaire ranged from 56.32 ± 25.42 to 72.48 ± 15.68. The overall HRQoL scores using the FACT-G and FACT-B instruments ranged from 60.78 ± 13.27 to 82.23 ± 12.55 and from 70.29 ± 13.33 to 108.48 ± 19.82, respectively. Factors affecting HRQoL of patients with breast cancer included age, education level, income, marital status, lifestyle, tumor stage, method, and treatment duration. Patient's income showed a consistent effect on HRQoL while the remaining factors reported inconsistent findings across the studies. In conclusion, the HRQoL of breast cancer patients in LMICs in Asia was low and affected by several sociodemographic factors which should be studied more in future research.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".