Perioperative Management of Patients with Diabetes and Cancer: Challenges and Opportunities
Bibliographic record
Abstract
BACKGROUND: Both diabetes and cancer are major global health issues that are among the leading causes of morbidity and mortality. There is a high prevalence of diabetes among cancer patients, many of whom require a surgical procedure. This review focuses on the operative complications in patients with diabetes and cancer, and the perioperative management of diabetes in cancer patients. METHODOLOGY: A literature search of articles in English-published between January 2010 and May 2024-was carried out using the databases PubMed, MEDLINE, Google Scholar, and the Cochrane Database of Systematic Reviews. The search primarily focused on the operative complications in patients with diabetes and cancer, and perioperative management strategies. RESULTS: The relationship between cancer and diabetes is complex; cancer patients have a high risk of developing diabetes, while diabetes is a risk factor for certain cancers. In addition, various cancer therapies can induce or worsen diabetes in susceptible patients. Many individuals with cancer and diabetes require surgery, and due to underlying diabetes, they may have elevated risks for operative complications. Optimal perioperative management for these patients includes managing perioperative glycemia and other comorbid illnesses, adjusting diabetic and cancer treatments, optimizing nutrition, minimizing the duration of fasting, supporting early mobilization, and providing patient education to enable self-management. CONCLUSIONS: While evidence is limited, optimal perioperative management for patients with both diabetes and cancer is necessary in order to reduce surgical complications. Future studies are needed to develop evidence-informed perioperative strategies and improve outcomes for these patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".