Influence of Diabetes Mellitus on Oncological Outcomes for Patients Living With Cancer
Bibliographic record
Abstract
PURPOSE: The purpose of this meta-analysis was to examine the association between preexisting diabetes in persons living with cancer on diabetes and oncology-related health outcomes. Understanding this association is of priority because the incidence of both cancer and diabetes mellitus is increasing worldwide. METHODS: A comprehensive review of the literature was conducted in collaboration with an expert health sciences librarian. Two authors independently conducted the screening, data collection, and extraction processes. The risk of bias was assessed using several tools, depending on the study design. Relative risks with 95% confidence intervals were calculated. The alpha threshold was 0.05. All analyses were performed using R statistical software (Metaphor and Demeter packages). RESULTS: A total of 45 studies met the selection criteria, but 23 were excluded from the synthesis because they did not have the ranked outcome or correct comparison (persons with and without diabetes), totaling 22 studies included in the meta-analysis. In comparison to participants without preexisting diabetes, participants with preexisting diabetes and cancer were found to have a significantly higher risk of infection and cardiovascular, neurological, gastrointestinal, hepatic, and renal complications. Concurrent preexisting diabetes and cancer were also associated with increased health care service utilization and length of hospital stay. CONCLUSION: The findings from this review highlight the importance of optimal concurrent management of both diseases by overcoming the compartmentalization of medical specializations through (1) integrated, multidisciplinary, shared, and coordinated clinical care pathways between oncology and diabetes health care providers/teams and (2) the continued development of evidence-based clinical guidelines.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".