Diabetes Comorbidity and Quality of Life in Patients with Cancer: A Prospective Study in an Integrative Oncology Setting
Bibliographic record
Abstract
Background: Research on quality of life (QoL)-related concerns among patients with both diabetes mellitus (DM) and cancer is limited. This study compared the QoL-related concerns and characteristics among chemotherapy-treated patients with cancer and DM to those without DM. Methods: Chemotherapy-treated patients were evaluated during integrative oncology (IO) consultations, which included evidence-based complementary therapies recommended by their healthcare providers to address quality of life (QoL) concerns. During these consultations, the participants were assessed for comorbidities, including diabetes mellitus (DM). QoL-related concerns were measured using the Edmonton Symptom Assessment Scale (ESAS) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Results: Of the 1171 patients referred for an IO consultation, 272 (23.2%) had an established diagnosis of DM. The DM patients were older, presented with more advanced stages of cancer, and had more chronic comorbidities (p < 0.001). While fatigue was the most frequently reported QoL-related concern in both groups, the patients with DM had more severe pain scores in the ESAS (4.9 vs. 4.4, p = 0.022) and lower ESAS well-being scores (5.9 vs. 5.5, p = 0.021). Conclusions: Chemotherapy-treated patients with cancer and DM are characterized by higher rates of comorbidities and report more severe scores for pain and for poorer general well-being. Oncologists and diabetologists should consider referring patients with both diagnoses for an IO consultation to address their QoL-related concerns. More research is needed to understand the impact of IO consultations and treatments on well-being among patients diagnosed with both DM and cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".