Objective Bayesian Snalysis for the Complementary Exponential Geometric Model Applied to Cancer Data
Bibliographic record
Abstract
In this paper we provide a reference Bayesian framework to a new two-parameter lifetime distribution with increasing failure rate, the complementary exponential geometric (CEG). To this end, we presented some of the main properties of this model and its characteristics related to the reliability analysis. A simulation study is performed to analyse the frequentist properties of credible intervals from the reference posterior distribution among of the standard error and mean square error (MSE) of estimations. The presented methodology is illustrated by the use of a real data set which presents the study of time until the cure of cervix lesions, that are precursors cancer lesions in the cervix. According to to INCA (Cancer National Institute), cervical cancer stands as the fourth cause of death among women in Brazil. Together with breast cancer, it is one of the most common malignancy affecting women worldwide. For this reason, patients must be carefully evaluated for metastatic disease. These data were collected in the Woman Clinic which is sited in Maring\'{a} city (Paran\'{a} State, Brazil).
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".