Measuring Comorbidity in Patients With Head and Neck Cancer
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: Comorbidities are diseases or conditions that coexist with a disease of interest. The importance of comorbidities is that they can alter treatment decisions, change resource utilization, and confound the results of survival analysis. OBJECTIVE: The objective of this study was to determine the best comorbidity index to use in survival analysis of patients with squamous cell carcinoma of the head and neck. METHOD: Four validated indexes, with very different methodologies (i.e., the Charlson Index, the Cumulative Illness Rating Scale, the Kaplan-Feinstein Classification, the Index of Co-existent Disease), were tested using data from 379 unselected consecutive patients with complete 3-year follow-up from the Kingston Regional Cancer Center. Kaplan-Meier analysis and Cox Proportional Hazards Regression were used to stratify patients into three levels of increasing severity of comorbidity for each index. The Proportion of Variance Explained and Receiver Operating Characteristics curves were used to compare the performance of the indexes. CONCLUSION: The Kaplan-Feinstein Classification was the most successful in stratifying patients in this population.
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) | 0.000 | 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