Prevalence Threshold and Temporal Interpretation of Screening Tests: The Example of the SARS-CoV-2 (COVID-19) Pandemic
Bibliographic record
Abstract
The curvilinear relationship between a screening test's positive predictive value (PPV) and its target disease prevalence is proportional. In consequence, there is an inflection point of maximum curvature in the screening curve defined as a function of the sensitivity and specificity beyond which the rate of change of a test's PPV declines sharply relative to disease prevalence. Herein, we demonstrate a mathematical model exploring this phenomenon and define the prevalence threshold point where this change occurs. Understanding where this prevalence point lies in the curve has important implications for the interpretation of test results, the administration of healthcare systems, the implementation of public health measures, and in cases of pandemics like SARS-CoV-2, the functioning of society at large. To illustrate the methods herein described, we provide the example of the screening strategies used in the SARS-CoV-2 (COVID-19) pandemic, and calculate the prevalence threshold statistic of different tests available today. This concept can help contextualize the validity of a screening test in real time, thereby enhancing our understanding of the dynamics of the current pandemic.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".