Response‐dependent two‐phase sampling designs for biomarker studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Two‐phase sampling designs are developed and investigated for use in the context of a rheumatology study where interest lies in the association between a biomarker with an expensive assay and disease progression. We derive optimal phase‐II stratum‐specific sampling probabilities for analyses from parametric maximum likelihood (ML), mean score (MS), inverse probability weighted (IPW) and augmented inverse probability weighted estimating equations (AIPW). The easy‐to‐implement optimally efficient design for the MS estimator is found to be asymptotically optimal for the IPW and AIPW estimators we consider, and is shown to result in efficiency gains over balanced and simple random sampling even when analyses are likelihood‐based. We further demonstrate the robustness of this optimal design and show that it results in very efficient estimation even when the model or parameters used in its derivation are misspecified. The Canadian Journal of Statistics 42: 268–284; 2014 © 2014 Statistical Society of Canada
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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 | 0.003 | 0.042 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it