Diabetes and Cancer: Risk, Challenges, Management and Outcomes
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus and cancer are commonly coexisting illnesses, and the global incidence and prevalence of both are rising. Cancer patients with diabetes face unique challenges. This review highlights the relationship between diabetes and cancer and various aspects of the management of diabetes in cancer patients. METHODS: A literature search using keywords in PubMed was performed. Studies that were published in English prior to July 2021 were assessed and an overview of epidemiology, cancer risk, outcomes, treatment-related hyperglycemia and management of diabetes in cancer patients is provided. RESULTS: Overall, 8-18% of cancer patients have diabetes as a comorbid medical condition. Diabetes is a risk factor for certain solid malignancies, such as pancreatic, liver, colon, breast, and endometrial cancer. Several novel targeted compounds and immunotherapies can cause hyperglycemia. Nevertheless, most patients undergoing cancer therapy can be managed with an appropriate glucose lowering agent without the need for discontinuation of cancer treatment. Evidence suggests that cancer patients with diabetes have higher cancer-related mortality; therefore, a multidisciplinary approach is important in the management of patients with diabetes and cancer for a better outcome. CONCLUSIONS: Future studies are required to better understand the underlying mechanism between the risk of cancer and diabetes. Furthermore, high-quality prospective studies evaluating management of diabetes in cancer patients using innovative tools are needed. A patient-centered approach is important in cancer patients with diabetes to avoid adverse outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".