Comparison of the Effectiveness of Polymerase Chain Reaction and Enzyme Immunoassay in Detecting <i>Chlamydia trachomatis</i> in Different Female Genitourinary Specimens
Bibliographic record
Abstract
BACKGROUND: In high-volume laboratories, enzyme immunoassay (EIA) is the most commonly used method of detecting Chlamydia trachomatis. The optimal specimen for detecting C trachomatis is a combined urethral and cervical swab. OBJECTIVE: To compare EIA with the combined urethral and cervical swab with polymerase chain reaction (PCR) on urine alone and urine mixed with cervical cells. PATIENTS AND METHODS: Phase 1 of the study included 752 sets of specimens used for comparison. In phase 2, another 212 samples of urine and urine plus cervical cells were added to the study for comparison of the 2 specimen types using PCR. RESULTS: In phase 1, 648 samples were negative and 76 were positive by all 3 methods and specimen combinations. Enzyme immunoassay was able to detect 81 positive samples (10.8%), whereas PCR on urine alone detected 97 positive samples (12.9%) and PCR on urine plus cervical cells detected 102 positive samples (13.6%), giving a sensitivity of 75%, 93.3%, and 98. 1% respectively. In phase 2, PCR on urine alone detected 119 positive samples (12.3%) and PCR on urine plus cervical cells detected 127 positive samples (13.1%), with a sensitivity of 92.2% and 98.5%, respectively. CONCLUSION: Polymerase chain reaction on urine alone or urine plus cervical cells is superior to EIA on combined cervical and urethral swabs. There is a slight advantage of adding cervical cells to the urine compared with the urine specimen alone when PCR is used as the assay for detection. The total inhibition rate in our female population is only 3.1% when PCR is used.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".