Methods for Evaluating Medical Tests and Biomarkers
Bibliographic record
Abstract
Background: The Test-Treatment Pathway has been proposed as a method to link test accuracy to downstream outcomes. By describing the clinical actions before and after testing, it illustrates how a test is positioned in the pathway, relative to other tests and diagnostics, and how the introduction of a new test may change the current diagnostics pathway. However, there is limited practical guidance on how to model such Test-Treatment Pathways. Methods: We selected the Patient -Index test-Comparator -Outcome (PICO) format, as also used elsewhere in evidence-based medicine, as a starting point for building the Test-Treatment Pathways. From there we developed a structured set of triggering questions. We defined these questions based on several brainstorm sessions and iteratively made changes to this basic structure after three rounds of user testing. During the user testing meetings, a pathway was drawn for each specific application. All sessions were recorded both on audio and video.
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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 | 0.008 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".