P3-S1.02 Evaluation of screening tests for<i>Chlamydia trachomatis</i>: bias associated with the patient infected status algorithm
Bibliographic record
Abstract
This study illustrates the bias associated with the use of an estimation approach called the patient infected status algorithm (PISA), which has been recently introduced and is increasingly used to produce sensitivity, and specificity estimates for Chlamydia trachomatis sand Neisseria gonorrhoea screening tests. PISA-based estimates have been published in the medical and microbiological literature and have been included in FDA approved package inserts of nucleic acid amplification tests for detecting Chlamydia trachomatis . In this study, we show that the PISA is an estimation procedure that can produce biased estimates of sensitivity, specificity and prevalence parameters. In a series of simulated scenarios we considered, none of the 95% CIs for PISA-based estimates of sensitivity and prevalence contained the true values. In addition, we show that the PISA-based estimates of sensitivity and specificity change markedly as the true prevalence changes. Thus, like earlier estimates such as discrepant analysis based estimates and unadjusted culture-based estimates of sensitivity and specificity, PISA based estimates are also biased.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".