A Review and Meta-analysis of Colorectal Cancer Utilities
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of utility weights for colorectal cancer (CRC) health states reported in the scientific literature and to determine the effects of disease factors, patient characteristics, and utility methods on utility values. METHODS: We identified 26 articles written in English and published from January 1980 to January 2013, providing 351 unique utilities for CRC health states elicited from 6546 unique respondents. The CRC utility data were analyzed using linear mixed-effects models with CRC type, stage, time to or from initial care, utility measurement instrument, and administration method as independent variables. RESULTS: In the base case model, the estimated utility for a patient with stage I to III CRC more than 1 year after surgery, rated using a self-administered time tradeoff instrument, was 0.90. Stage, time to or from initial care, and utility measurement instrument were associated with statistically significant utility differences ranging from -0.19 to 0.02. Utilities for patients with stage IV cancer were 0.19 lower (P < 0.001) than for those with stage I to III cancer. Utilities elicited at more than 1 year after surgery were 0.05 higher than those elicited at 3 months after surgery (P = 0.008). Estimates of differences between utility measurement instruments were sensitive to how repeated scores in the same patient group were treated, and other findings were sensitive to how the disease stage was modeled and method of administration. CONCLUSIONS: Variations in reported utilities are associated with factors such as cancer stage, time to or from initial care, and utility measurement instrument. More research is needed to study why apparently similar patients report different quality of life.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".