Diabetes and Cancer: Impact on Health-Related Quality of Life
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore whether three factors (physical function, mental health, and social function) of health-related quality of life (HRQOL) are impacted differently in patients with cancer and diabetes when compared to those with cancer who do not have diabetes at the beginning of chemotherapy. DESIGN: Secondary analysis using baseline data from two randomized, controlled trials. SETTING: Two comprehensive cancer centers, one community cancer oncology program, and six hospital-affiliated community oncology centers. SAMPLE: 661 patients aged 21 years or older with a solid tumor cancer or lymphoma undergoing cancer treatment. METHODS: Baseline data from both randomized, controlled trials were used. The SF-36® was used to measure physical function, mental health, and social function. Analysis included descriptive statistics and a general linear model. MAIN RESEARCH VARIABLES: Presence or absence of diabetes and physical function, social function, and mental health. FINDINGS: Patients with cancer and diabetes had significantly lower levels of physical function (p < 0.001) when compared to those who had cancer without diabetes. The interaction of diabetes and age was found to be significantly predictive of mental health (p < 0.05). CONCLUSIONS: The presence of diabetes negatively impacts physical function and mental health in patients undergoing chemotherapy. IMPLICATIONS FOR NURSING: Nurses should be aware of diabetes' effect on HRQOL in patients with cancer. In addition, nurses may need to intervene earlier for patients with diabetes and cancer to maintain or improve their quality of life.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".