Examining Biliary Acid Constituents among Gall Bladder Patients: A Bayes Study Using the Generalized Linear Model
Bibliographic record
Abstract
The generalized linear model is an important class of models that has wide variety of applications mainly because of its inherent flexibility and generality. The present paper provides an important application of GLM in order to examine different constituents of bile acid in the development of gallstones as well as carcinoma among the gallbladder patients. These constituents may be broadly categorized as primary and secondary bile acids. The paper, in fact, considers two particular cases of GLM based on normal and gamma modelling assumptions and provides the complete Bayes analysis using independent but vague priors for the concerned model parameters. It then analyzes a real data set taken from SS Hospital, Banaras Hindu University, with primary (secondary) bile acids as response variables and secondary (primary) bile acids as the predictors. The authenticity of the assumed models for the given data set is also examined based on predictive simulation ideas.
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How this classification was reachedexpand
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 | 0.005 | 0.041 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".