Predictive Factors of Health-Related Quality of Life in Patients with Liver Cancer
Bibliographic record
Abstract
Background: Primary liver cancer is one of the most malignant and deadly cancers, which reduces the quality of life. Health-related quality of life (HRQOL) is an important criterion for evaluating the health care, controlling diseases, outcomes, benefits, and disadvantages of treatments, quality of medical treatment and nursing care, and screening individuals at risk for physical and psychological problems. The aim of this study was to investigate the predictive factors of HRQOL in patients with liver cancer. The results of this study can improve the quality of life of the patients and the level of health care. Materials and Methods : This study was performed on 272 patients referred to Imam Khomeini Hospital in Tehran (2018-2019), by using convenience sampling method. For assessing patients, we used Function Assessment Cancer Therapy, Hospital Anxiety and Depression Scale, Fatigue Severity Scale, Edmonton Symptoms Assessment Scale, and Characteristics Demographic. Data were collected in two stages (before and one month after treatment). In order to validate these tools, previous studies were cited and their reliability was assessed by test-retest. Data were analyzed by using SPSS software version 21. Descriptive statistics, correlation test, paired t test, and linear regression were used as appropriated. Results : The mean HRQOL score in patients with liver cancer was 58.11 (±17.31). Effective factors in HRQOL was the type of treatment, physical and psychological consequences, and some demographic characteristics. Conclusion: The present study showed that many factors can predict HRQOL in patient with liver cancer. Also, awareness of these factors provides the basis for developing effective care plans to promote HRQOL and increase life expectancy in affected patients. These factors are very important because based on them HRQOL of patient with liver cancer can be estimated with 59/8% accuracy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".