191-LB: Comorbid Diabetes and Cancer: Differences by Patient Characteristics and Symptom Outcomes
Bibliographic record
Abstract
Co-morbid Diabetes and Cancer: Differences by Patient Characteristics and Symptom Outcomes More than 32 million Americans have diabetes and 1.8 million new cancer diagnoses are expected in 2020. Epidemiologically, a strong link exists between diabetes and several forms of cancer including pancreatic, colorectal, lung, and liver cancers. As a result of early cancer diagnosis and improved treatment modalities cancer survivors are living longer with other chronic diseases including diabetes. Since knowledge gaps exist regarding potential effects of having both diabetes and cancer, we examined demographic characteristics and Edmonton Symptom Assessment Scale-revised scores of 306 adult palliative care patients with cancer, 42 of whom had diabetes. The mean age of those with and without diabetes did not differ significantly (67.3±5.4 vs. 66.3±7.8, respectively; p=.35). Among these 306 patients, the prevalence of diabetes was 16% among males versus 12% among females (p=.40); 20% among African Americans, 11% among non-Hispanic Whites, and 23% among other races (p=.04). Those with diabetes reported significantly lower pain (2.7±2.6) than those without diabetes (3.6±2.9, p=.05). However, there were no significant differences between those with and without diabetes in depression (2.3±2.6 vs. 2.1±2.6, p=.69) and well-being (3.2±2.6 vs. 3.6±2.5, p=.32). Prevalence differed significantly for patients with diabetes versus those without diabetes for pancreatic cancer (14% vs. 5%, p=.04) but not for lung (26% vs. 17%, p=.20), colorectal (10% vs. 5%, p=.29), or liver (7% vs. 2%, p=.08) cancers. Findings revealed few differences across demographic characteristics and pain, depression and well-being among palliative care patients with and without diabetes. Additional prospective research is needed to investigate the effects of comorbid diabetes and cancer in a larger sample of subjects with a focus on specific cancer subtypes. Disclosure L. Scarton: None. D.J. Wilkie: None. G. Fitchett: None. L. Emanuel: None. G. Handzo: None. Y. Yao: None. H.M. Chochinov: None.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".