Acute Diabetes-Related Complications in Patients Receiving Chemoradiotherapy for Head and Neck Cancer
Bibliographic record
Abstract
Patients with cancer and diabetes face unique challenges. Limited data are available on diabetes management in patients undergoing concurrent chemoradiotherapy (CCRT), a curative intent anticancer therapy commonly associated with glucocorticoid administration, weight fluctuations and enteral feeds. This retrospective case–control study examined the real-world incidence of acute diabetes-related complications in patients with head and neck cancer receiving CCRT, along with the impact of diabetes on CCRT tolerance and outcomes. Methods: Consecutive patients with head and neck squamous cell or nasopharyngeal cancer who underwent definitive or adjuvant CCRT between 2010 and 2019 at two large cancer centers in Australia were included. Clinicopathological characteristics, treatment complications and outcomes were collected from medical records. Results: Of 282 patients who received CCRT, 29 (10.3%) had pre-existing type 2 diabetes. None had type 1 diabetes. The majority (74.5%) required enteral feeding. A higher proportion of patients with diabetes required admission to a high-dependency or intensive care unit (17.2 versus 4.0%, p = 0.003). This difference was driven by the group who required insulin at baseline (n = 5), of which four (80.0%) were admitted to a high-dependency unit with diabetes-related complications, and three (60.0%) required omission of at least one cycle of chemotherapy. Conclusions: Patients with diabetes requiring insulin have a high risk of acute life-threatening diabetes-related complications while receiving CCRT. We recommend multidisciplinary management involving a diabetes specialist, educator, dietitian, and pharmacist, in collaboration with the cancer care team, to better avoid these complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".