Impact of Type 2 Diabetes Mellitus on Survival in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Objective To identify any association between type 2 diabetes mellitus (T2DM) and survival outcomes for mucosal squamous cell carcinoma of the head and neck. An association has been demonstrated between T2DM and cancer outcomes at numerous sites, but data for the head and neck are limited. Improving our understanding of the impact that diabetes has on head and neck cancer survival is relevant for making treatment decisions and counseling patients regarding prognosis. Study Design Retrospective cohort study. Setting Academic tertiary referral head and neck cancer center. Subjects and Methods By accessing data retrospectively from prospectively collected databases at the Princess Margaret Cancer Centre, patients were studied who were treated for mucosal head and neck squamous cell cancer between January 2005 and December 2011. Collection of clinical, pathologic, and survival data was completed with an emphasis on T2DM. Results Of 2498 patients identified in the study period, 319 (12.8%) had T2DM. Five-year overall survival was not different between the diabetic (64%, 95% CI = 58%-71%) and nondiabetic (67%, 95% CI = 65%-69%; P = .078) groups. Furthermore, cause-specific survival did not demonstrate a statistically significant difference between groups (diabetic: 84%, 95% CI = 79%-88%, vs nondiabetic: 84%, 95% CI = 82%-86%; P = .67). Conclusion Despite contradictory evidence at other cancer sites, the presence of T2DM alone does not appear to adversely affect cancer survival outcomes in head and neck squamous cell cancer. This is encouraging for the diabetic patients with head and neck squamous cell cancer, and it provides guidance for the multidisciplinary team that treats them.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".