Using Available Information in the Assessment of Diagnostic Pro tocols
Bibliographic record
Abstract
A new binary screening or diagnostic test may be combined sequentially with an existing test using either a believe the positive or believe the negative protocol.Interest then lies in estimating the properties of the new combined protocol and in comparing the new protocol with the existing test via sensitivity, specificity, or likelihood ratios that capture the trade-off between sensitivity and specificity.We consider a paired assessment study with complete verification via a gold standard.Our goal is to quantify the gain in precision for the estimators of the sensitivity, specificity and the ratio of likelihood ratios in protocols when baseline information on the performance of the existing test is available.We find maximum likelihood estimators of the quantities of interest and derive their asymptotic standard deviations.The methods are illustrated using previously published mammography and ultrasound test results from a cohort of symptomatic women.We find that incorporating baseline information has a large impact on the precision of the estimator for the specificity of the believe the positive protocol and of the sensitivity of the believe the negative protocol.Including available baseline information can improve the precision of estimators of the sensitivity, specificity, and the ratio of likelihood ratios and/or reduce the number of subjects needed in an assessment study to evaluate the protocol.
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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 | 0.107 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".