The Human Immune Response to Streptococcal Extracellular Antigens: Clinical, Diagnostic, and Potential Pathogenetic Implications
Bibliographic record
Abstract
BACKGROUND: Determination of an immune response to group A Streptococcus (GAS) antigens, frequently anti-streptolysin O and anti-DNase B, is crucial for documentation of bona fide GAS infection. Although the importance of immunologic confirmation of infection is widely accepted, the immediate and long-term immunokinetics of the human antibody response are incompletely documented and poorly understood. METHODS: Pediatric study participants (n = 160) were followed during a 2-year study with monthly throat cultures (n = 3491) and blood samples (n = 1679) obtained every 13 weeks. Recovered GAS were characterized; serum anti-streptolysin O and anti-DNase B antibody titers were determined. Antibody titers and GAS culture results were temporally correlated and analyzed. RESULTS: The analyses clearly document, in some instances for the first time, that an increase in antibody titer more accurately defines infection than does an absolute titer (eg, "upper limit of normal"), that antibody titers can remain elevated for many months even without GAS, and that some individuals may harbor GAS continuously for months or years without symptoms of infection and without an associated immune response. Measuring 2 different antibodies is more accurate in defining infection. CONCLUSIONS: Single time-point cultures and single antibody titers are often misleading. Sequential samples more accurately define infection, allowing correlation of titer increases with temporal confirmation of GAS acquisition. Understanding kinetics of the immune response(s) to GAS infection is necessary in formulating accurate clinical diagnostic conclusions, to appropriate design of clinical and epidemiological studies examining the association of GAS with subsequent sequelae, and to providing insight into pathogenetic mechanisms associated with this important human pathogen.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".