A Bayesian Analysis of the True Sensitivity of a Temporal Artery Biopsy
Bibliographic record
Abstract
PURPOSE: The temporal artery biopsy (TAB) has long been the standard for diagnosing temporal arteritis (TA), but in practice this test is less than 100% sensitive; false-negative biopsy results are known to occur. The true sensitivity of a single TAB cannot be directly observed, because there is no true gold standard for comparison. The authors propose a mathematical method for calculating the true sensitivity of the TAB, using data from published bilateral TAB RESULTS: METHODS: Based on Bayesian methodology, this statistical technique can be used to calculate the true sensitivity of a single TAB with data from studies reporting the results of bilateral simultaneous TABs. This technique also allows for calculation of the true prevalence of TA in a study population. Bootstrap techniques are used to provide confidence intervals. This technique is applied to data derived from four studies in the literature. results. With this methodology, the sensitivity of a single TAB is calculated to be 87.1% (95% confidence interval, 81.8%-91.7%). CONCLUSIONS: Knowledge of the true sensitivity of any imperfect test is necessary for an accurate decision analysis, because it can affect the optimal diagnostic-therapeutic pathway. Although few studies report results of bilateral simultaneous TABs, such data are important because they permit the calculation of the true TAB sensitivity. The authors believe that this mathematical method is superior to observational methods (e.g., clinical criteria) for estimating the true sensitivity of a TAB.
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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 | 0.070 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".