Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test
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
BACKGROUND: Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW-SP, a urinary antigen test, as an add-on to standard cultures may not only increase diagnostic yield but also increase costs. OBJECTIVE: To estimate the sensitivity and specificity of BinaxNOW-SP and subsequently estimate the cost-effectiveness of adding BinaxNOW-SP to the diagnostic work-up. DESIGN: We fit a Bayesian latent-class meta-analysis model to obtain estimates of BinaxNOW-SP accuracy that adjust for the imperfect accuracy of culture. Meta-analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW-SP to the work-up per case correctly diagnosed. RESULTS: The BinaxNOW-SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67-0.83) and a pooled specificity of 0.96 (95% CrI, 0.92-0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW-SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars. CONCLUSIONS: The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence-based approach.
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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 | 0.017 | 0.127 |
| 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 | 0.001 | 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