The impact of comorbidity on the survival of patients with squamous cell carcinoma of the head and neck
Bibliographic record
Abstract
BACKGROUND: In North America, cigarette smoking and/or alcohol consumption not only cause head and neck cancer, they also cause many of the other diseases, illnesses, and conditions, also known as comorbidities, frequently found in our patients. Comorbidities can influence treatment decision making and treatment outcome. The aim of this study is to quantify the increased risk of comorbidity in our patients. METHOD: The survival of 655 consecutive patients with squamous cell carcinoma from a regional cancer center is analyzed. We compare the survival curves for all-cause death, death from cancer, and death from noncancer causes to the expected survival of age/sex-matched populations of Ontario residents, Canadian smokers, and Canadian nonsmokers. RESULTS: Of those patients who had not survived 5 years, 59% died of their index tumor, 23% would have been expected to die if they did not have head and neck cancer, and 18% died of the increased comorbidity associated with being a patient with head and neck cancer. DISCUSSION: Comorbidity, and specifically the increased comorbidity found in patients with head and neck cancer, is an important factor in overall survival.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".