Performance Characteristics of Basophil Activation Tests for Diagnosing Penicillin Allergy: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Approximately 10% of the global population identify themselves as penicillin allergic, yet 90% are not truly allergic and could safely tolerate penicillin. There is no simple way to identify these people. Current in vitro diagnostics include specific immunoglobulin E (with a sensitivity of 19% and specificity of 97%) and a basophil activation test (BAT) with undefined sensitivity and specificity. OBJECTIVE: To define the sensitivity and specificity of BAT in the diagnosis of penicillin allergy METHODS: We searched PubMed and EMBASE from inception to April 2, 2023, for original studies evaluating the performance characteristics of BAT for penicillin allergy in adults. Study selection, data extraction, risk of bias, assessment with QUADAS-2 tool, certainty assessment with Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology were performed independently, in duplicate. Meta-analysis was performed using Reitsma methodology. RESULTS: Twenty-two studies fulfilled the inclusion criteria. Twelve used the same positive threshold giving a summary point sensitivity 51% (95% confidence interval [95% CI]46%-56%) and specificity 89% (95% CI 85%-93%). Significant risk of bias was identified owing to patient selection. GRADE certainty of evidence rated sensitivity very low due to imprecision and specificity as low. There was great heterogeneity in methods used. Use of 1,000 basophils per test did not improve performance above 500 basophils. CONCLUSIONS: BAT sensitivity is highly variable across studies and remains too low to be considered as a routine element of clinical practice. BAT specificity is not as good as specific immunoglobulin E in penicillin allergy diagnosis. Significant further work is required in this field before clinical application of BAT in routine practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".