Multi-System Inflammatory Syndrome in Neonates (MIS-N) - Clinical Profile and Outcomes - A Prospective Cohort Study
Bibliographic record
Abstract
Aims: To analyze the clinical spectrum in Neonates with MIS-N based on the time of presentation and also to assess the use of immunomodulator therapy in MIS-N. Subjects and Methods: We studied 100 neonates delivered at BLDE (DU) Shri B M Patil Medical College Hospital admitted to Level III-A NICU from JULY 2020 to MAY 2021. 98 neonates had high titers of IG G antibodies and were negative for COVID Antigen. We categorized the cohorts into EARLY MIS-N (<72 hrs) and LATE MIS-N (>72 hrs). Results: 58 presented as EARLY MIS-N with Respiratory distress (RD) in 40 (70%), cardiac dysfunction 34 (60%), PPHN 12(20%), Fever 12(20%), seizures 12(20%), encephalopathy in 6(10%), sepsis-like features 6(10%), had elevated inflammatory markers like CRP (30%), D-Dimer (70%), Ferritin (30%), cardiac biomarkers like BNP (60%), LDH (30%) and ECHO showing LV dysfunction in 50%. LATE MIS-N presented mostly with fever 28(70%), sepsis-like features 24(60%), Respiratory Distress in 16(40%), cardiac dysfunction 12 (30%), hypoglycemia 4(10%), parotitis 4(10%), had significantly elevated inflammatory markers like CRP (70%), D-Dimer (50%), Ferritin (70%), cardiac biomarkers like BNP (40%), LDH (20%) and ECHO showing LV dysfunction in 20%, dilated coronaries in 20 %, PPHN in 10%. Oxygen and respiratory support requirements were higher in EARLY presenters and IVIG and steroid requirements were more in LATE presenters. Conclusion: We observed that maternal SARS-COV-2 antibodies transferred transplacentally and neonatal antibodies acquired after COVID-19 infection can cause MIS-N in neonates. Immunomodulator therapy is required in severe cases of MIS-N only.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".