Use of cure fraction model for the survival analysis of uterine cancer patients
Bibliographic record
Abstract
Objectives: In population-based cancer studies a cure fraction model classifies patients into those who survive the cancer and those who encounter excess mortality risk compared to the general population [1]. In this presentation we report the proportion cured and the relative survival pattern for patients diagnosed with uterine cancer in Canada over the period of 1992-2005. Methods: We used a non-mixture cure fraction model to estimate the cure fraction rate and the relative survival among “uncured†patients [1]. Then, we predicted the cure fraction rate and median survival for each age group based on the year of diagnosis. Results: Relative survival and cure fraction rate decreased with age but increased gradually over time. Relative survivals for Eastern Canada and Ontario were lower compared to the other regions. The same applies to the comparison between cure fraction rates between the geographical regions. Conclusion: This is the first study using cure fraction model for analysis of uterine cancer. Although there are some limitations attached to this model, it is flexible enough to be used with different parametric distributions and to include different link functions for relative survival analysis. [1] Lambert PC. Modeling of the cure fraction in survival studies. Stata Journal 2007;7:1-25.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".