Chlamydia antibody testing and diagnosing tubal pathology in subfertile women: an individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: The Chlamydia IgG antibody test (CAT) shows considerable variations in reported estimates of test accuracy, partly because of the use of different assays and cut-off values. The aim of this study was to reassess the accuracy of CAT in diagnosing tubal pathology by individual patient data (IPD) meta-analysis for three different CAT assays. METHODS: We approached authors of primary studies that used micro-immunofluorescence tests (MIF), immunofluorescence tests (IF) or enzyme-linked immunosorbent assay tests (ELISA). Using the obtained IPD, we performed pooled receiver operator characteristics analysis and logistic regression analysis with a random effects model to compare the three assays. Tubal pathology was defined as either any tubal obstruction or bilateral tubal obstruction. RESULTS: We acquired data of 14 primary studies containing data of 6191 women, of which data of 3453 women were available for analysis. The areas under the curve for ELISA, IF and MIF were 0.64, 0.65 and 0.75, respectively (P-value < 0.001) for any tubal pathology and 0.66, 0.66 and 0.77, respectively (P-value = 0.01) for bilateral tubal pathology. CONCLUSIONS: In Chlamydia antibody testing, MIF is superior in the assessment of tubal pathology. In the initial screen for tubal pathology MIF should therefore be the test of first choice.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".