Maternal and neonatal IgG against Klebsiella pneumoniae are associated with lower risk of neonatal sepsis: A case-control study of hospitalized neonates in Botswana
Bibliographic record
Abstract
Sepsis is the leading postnatal cause of neonatal mortality worldwide. Globally Klebsiella pneumoniae is the leading cause of sepsis in hospitalized neonates. This study reports the development and evaluation of an ELISA for anti-Klebsiella IgG using dried blood spot (DBS) samples and evaluates the association of anti-Klebsiella IgG (anti-Kleb IgG) antibodies in maternal and neonatal samples with the risk of neonatal sepsis. Neonates and their mothers were enrolled at 0-96 hours of life in the neonatal unit of a tertiary referral hospital in Gaborone, Botswana and followed until death or discharge to assess for episodes of blood culture-confirmed neonatal sepsis. Neonates with sepsis had significantly lower levels of Kleb-IgG compared to neonates who did not develop sepsis (Mann-Whitney U, p = 0.012). Similarly, samples from mothers of neonates who developed sepsis tended to have less Kleb-IgG compared to mothers of controls. The inverse correlation between Kleb-IgG levels and all-cause bacteremia suggests that maternal Kleb-IgG may be protective through cross-reactivity with common bacterial epitopes. These data support the continued use of immunoglobulin assays using DBS samples to explore the role of passive immunity on neonatal sepsis risk and reaffirm the critical need for research supporting the development of maternal vaccines for neonatal sepsis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".