Comparison of a new commercial EIA kit and the microimmunofluorescence technique for the determination of IgG and IgA antibodies to <i>Chlamydia pneumoniae</i>
Bibliographic record
Abstract
Chlamydia pneumoniae infection is often diagnosed by analyzing specific antibodies to C. pneumoniae in sera. The method which has been used as the reference method, or "gold standard", the microimmunofluorescence test (MIF), demands a high level of experience for proper interpretation. A number of commercial enzyme immunoassay (EIA) tests have been introduced to the market in the past few years. These provide objective reading of titers, but are genus specific and not species specific. The latest EIA introduced, LabSystems EIA for C. pneumoniae, was investigated using several groups of clinically relevant patient sera in a comparison with MIF. It was found that the LabSystems EIA did not discriminate between antibodies to C. trachomatis and C. pneumoniae when tested with sera containing high titers of C. trachomatis antibodies. The correlation between C. pneumoniae EIA and MIF IgG and IgA titers was, however, good in the patient groups not having a high background of C. trachomatis antibodies: hypertensives, n= 199 and patients with chronic C. pneumoniae infections and ischaemic heart disease, n=33. In conclusion, the LabSystems EIA is a method which can be useful for screening populations with low prevalences of C. trachomatis/C. psittaci infection for antibodies to C. pneumoniae. It cannot replace the MIF test due to the lack of discrimination between different chlamydial antibody types.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".