The Estimand Framework in Diagnostic Accuracy Studies
Bibliographic record
Abstract
Diagnostic accuracy studies evaluate how well a diagnostic test can detect or rule out a medical condition. Different events can interfere with the conduct of the test, affecting the test result. Before starting a diagnostic test accuracy study, the clinical question of interest should be precisely defined. Based on that, strategies can be chosen for dealing with the interfering event. We introduce six different strategies for how such events could be handled. We introduce the estimand concept for diagnostic accuracy studies, which consists of the attributes population, target condition, index test, accuracy measure, and the strategies for handling interfering events. The estimand determines which effect is estimated based on the study objective. To bridge the gap between the clinical study objective and the method for the estimation, we illustrate the necessary steps using a fictitious computed tomography scan study. The defined estimand improves the structure of the planning phase, enhances the interdisciplinary exchange, and supports the interpretation based on the study objective.
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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 | 0.089 | 0.876 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".