Pediatric Bartonella henselae Infection
Bibliographic record
Abstract
BACKGROUND: Bartonella henselae serology is commonly used to diagnose cat-scratch disease (CSD). Titers above a threshold for positivity suggest either a recent or remote infection. Recent infection can be confirmed by a 4-fold rise in the convalescent titer in some cases. Many atypical presentations attributed to CSD utilize a low threshold for positivity without supportive evidence from convalescent sera or supplemental testing, raising a concern for the overdiagnosis of CSD. METHODS: We conducted a retrospective chart review of immunocompetent pediatric patients at the Hospital for Sick Children, Toronto, spanning an 11-year period. A total of 154 cases were included with serologic titers ≥1:128. These were divided into 3 groups: group 1 = 1:128, group 2 = 1:256, and group 3 ≥ 1:512. Cases within groups were evaluated with respect to cat contact, clinical presentation, further testing, and final diagnosis. RESULTS: One-third of patients with a titer of 1:128 had an alternative diagnosis. Most cases with a titer of 1:128 or 1:256 did not have convalescent serologic testing performed. Within these 2 groups, only 1 case had a 4-fold rise in the convalescent titer. A trend of decreasing number of cases with alternative diagnoses (P = 0.03) and increasing number of cases presenting with regional lymphadenopathy (P = 0.07) was associated with higher titers in group 3 compared with group 1. CONCLUSION: Concerns about the serologic diagnosis of CSD include the use of low titers for positivity, incomplete diagnostic evaluation, and the lack of convalescent serologic testing. We propose a clinical guide to assist in managing suspected cases of CSD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".