Additional file 1 of Comparison of measles IgG enzyme immunoassays (EIA) versus plaque reduction neutralization test (PRNT) for measuring measles serostatus: a systematic review of head-to-head analyses of measles IgG EIA and PRNT
Bibliographic record
Abstract
Additional file 1: Supplemental Table 1. PRISMA statement for a systematic literature search checklist. Supplemental Table 2A. Studies evaluating EIA compared to PRNT. Supplemental Table 2B. Studies evaluating EIA compared to PRNT. Supplementary Table 3. Mediandiagnostic accuracy of EIA compared to PRNT by assay type and study quality. Supplementary Table 4. Diagnostic accuracy measures reported in medium quality studies. Supplementary Figure 1. Summary of Quality Assessment of Diagnostic Accuracy Studiesresults. Supplementary Figure 2. HSROC curves for measles EIA acompared to PRNT for high quality studies evaluating Siemens Enzygnost EIA kits. Supplementary Figure 3. Diagnostic accuracy of EIA compared to PRN reported in medium quality studies. Supplementary Figure 4. Diagnostic accuracy of EIA assays compared to PRN by assay type. Supplementary Figure 5. Diagnostic accuracy of EIA compared to PRNT when EIA equivocals are re-classified, compared to results reported in high quality studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.751 | 0.030 |
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".