Threshold for basophil activation test positivity in neuromuscular blocking agents hypersensitivity reactions
Bibliographic record
Abstract
BACKGROUND: Several different criteria for the positivity of the flow-assisted Basophil Activation Test (BAT) for the diagnosis of Neuromuscular Blocking Agents (NMBA) hypersensitivity reactions have been used in past studies. Our aims were to determine the threshold for BAT positivity expressed as the stimulation index (SI, calculated as the percentage of activated basophils after stimulation with NMBA divided by the number of basophils with no NMBA stimulation) and as the percentage of activated basophils, and to determine the sensitivity and specificity of BAT for NMBAs. METHODS: 22 consenting adult patients with previous intraanaesthetic NMBAs-related hypersensitivity reactions were tested for the culprit drug. 34 controls who tolerated NMBAs were similarly tested. BAT was performed using Flow2Cast technique and the up-regulation of the CD63 marker on the basophils was measured using Cell Quest programme (FACSCalibur Becton Dickinson, USA). Receiver operating characteristics curve (ROC) analysis was performed. RESULTS: ROC curve analysis for BAT results versus history yields a stimulation index of 1.76 as the optimal threshold, with an AUC of 0.81 (CI 95% 0.69-0.93, p < 0.01) and a percentage of activated basophils > 5.01%, with an AUC of 0.84 (CI 95% 0.72-0.95, p < 0.01). Considering both thresholds (the SI ≥ 1.76 together with the percentage of activated basophils > 5%) as diagnostic criteria, 15 patients had positive BAT, the overall BAT sensitivity being 68.18% (CI 95% 45.11-82.26%). None of the controls fulfilled both criteria and the specificity of the test was 100% (CI 95% 87.35-100%). CONCLUSIONS: With a stimulation index ≥ 1.76 and a percentage of activated basophils > 5.01% as threshold, the performance of BAT for NMBAs yields 68.18% sensitivity and 100% specificity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".