Bayesian semi-parametric joint modeling of biomarker data with a latent changepoint: assessing the temporal performance of Enzyme-Linked Immunosorbent Assay (ELISA) testing for paratuberculosis
Bibliographic record
Abstract
In this paper, we develop a class of semi-parametric statistical models that can be used for the important problem of analyzing longitudinal biomarker data with the purpose of quantifying their diagnostic capabilities, as a function of time from infection. We focus on the complicated problem where there is no gold standard assessment of the actual timing of infection/disease onset (our change point), which provides additional motivation for considering a second, binary test, in order to make it easier to estimate the change points for individuals that become diseased. An important additional feature of our model is its nonparametric part, which allows for distinct biomarker responses to the insult of infection/disease. In our case, the model allows for the possibility of an unknown number of clusters of individuals, each with distinct slopes corresponding to distinct biological reactions. Clusters with steeper slopes would correspond to individuals that could be diagnosed sooner than those with more gradual slopes.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".