Value of amino‐terminal pro B‐natriuretic peptide in diagnosing Kawasaki disease
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to investigate the diagnostic value of the N-terminal B-type natriuretic peptide (NT-proBNP) in acute Kawasaki disease (KD) given that the clinical criteria and the current basic laboratory tests lack the necessary specificity for accurate diagnosis. METHODS: Basic biological tests and serum NT-proBNP levels obtained from acute KD patients were compared to that of febrile controls. NT-proBNP was considered abnormal based on the following definitions: above a cut-off determined on receiver operator characteristic (ROC) analysis, above the upper limit for age, or above 2 SD calculated from healthy children. Analyses were also performed for KD cases with complete or incomplete criteria combined and separately. RESULTS: There were 81 patients and 49 controls aged 3.60 ± 2.77 versus 4.25 ± 3.88 years (P= 0.69). ROC analysis yielded significant area under the curve for NT-proBNP. The sensitivity, specificity, positive and negative predictive values were 70.4-88.9%, 69.4-91.8%, 82.8-93.4%, and 65.2-79.1%. The odds ratios based on NT-proBNP definitions varied between 18.13 (95% confidence interval [CI]: 7.21-45.57), 20.82 (95%CI: 8.18-53.0), and 26.71 (95%CI: 8.64-82.57; P < 0.001). Results were reproducible for cases with complete or incomplete criteria separately. CONCLUSION: NT-proBNP is a reliable marker for the diagnosis of KD. Prospective clinical studies with emphasis on NT-proBNP in a diagnostic algorithm are needed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".