Health-related quality of life and associated factors among cervical cancer patients at Tikur Anbessa specialized hospital, Addis Ababa, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Cancer of the cervix is the most frequent cancer among women in Ethiopia. The disease burden and its treatment adversely affects patients' health-related quality of life (HRQoL). We aimed to investigate the HRQoL and its predictors among cervical cancer patients in Ethiopia. METHODS: A hospital-based cross-sectional study was conducted from January to June 2018 at the oncology unit of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. A total of 404 cervical cancer patients were interviewed using validated Amharic version of the European Organization for Research and Treatment of Cancer module (EORTC QLQ-C30), cervical cancer module (EORTC QLQ-CX24), and Euro Quality of Life Group's 5-Domain Questionnaires 5-Levels (EQ-5D) questionnaires. ANOVA test was used to determine the effect of patients' characteristics on mean scores of the different domains of HRQoL and stepwise multivariable logistic regression was performed to identify predictors of HRQoL. Coefficients of level-specific utility values obtained from a hybrid regression model for the Ethiopian general population were used to compute utility. RESULTS: The mean age of patients was 52.1 ± 10.4 years and 379 (93.8%) of the patients were receiving service at the outpatient clinic. About one-third (35%) of patients were diagnosed with stage IV cervical cancer. Mean global health status/QoL, mean utility and visual analog scale scores were 48.3 ± 23.77, 0.77 and 65.7 ± 20.83, respectively. Physical functioning (AOR = 4.98, 95% CI:2.16-11.49), emotional functioning (AOR = 5.25, 95% CI:2.26-12.17), pain (AOR = 5.79, 95% CI:2.30-14.57), and symptom experience (AOR = 4.58, 95% CI:1.95-10.79) were associated with patients' HRQoL. CONCLUSIONS: Cervical cancer significantly affects patient's HRQoL and hence, efforts to improve HRQoL should be commenced especially in terms of physical and emotional functioning, pain, and symptom experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".