Preventable Diabetic Complications After a Cancer Diagnosis in Patients With Diabetes: A Population-Based Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A cancer diagnosis may disrupt diabetes management, increasing the risk of preventable complications. The objective was to determine whether a cancer diagnosis in patients with diabetes is associated with an increased risk of diabetic complications. METHODS: This retrospective cohort study using health care data from Ontario, Canada, included persons age 50 years or older diagnosed with diabetes from 2007 to 2011 and followed until 2014. We examined the effects of cancer as a time-varying covariate: breast cancer (in women), prostate cancer (in men), colorectal cancer, and other cancers (in men and women). Each cancer exposure was categorized as stage I-III, IV, or unknown, and by time since cancer diagnosis (0-1 year, >1-3 years, and >3 years). The primary outcome was hospital visits for diabetic emergencies. Secondary outcomes were hospital visits for skin and soft tissue infections and cardiovascular events. RESULTS: Of 817 060 patients with diabetes (mean age = 64.9 +/- 10.7 years), there were 9759 (1.2%) colorectal and 45 705 (5.6%) other cancers, 6714 (1.7%) breast cancers among 384 257 women and 10 331 (2.4%) prostate cancers among 432 803 men. For all cancers except stage I-III prostate cancer, rates of diabetic complications were significantly higher zero years to one year after diagnosis compared with no cancer (adjusted relative rates ranging from 1.26, 95% confidence interval [CI] = 1.08 to 1.49, to 4.07, 95% CI = 3.80 to 4.36); these differences were attenuated in the subsequent periods after cancer diagnosis. CONCLUSIONS: Patients with diabetes are at increased risk for preventable complications after a cancer diagnosis. Better diabetes care is needed during this vulnerable period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it