Infectious Causes of Stillbirths: A Descriptive Etiological Study in Uganda
Bibliographic record
Abstract
Abstract Background Every year an estimated 2–3 million babies are stillborn, with a high burden in Africa. Infection is an important driver of stillbirth. There is a lack of data on the bacterial causes of stillbirth in Uganda, contributing to a lack of interventions such as effective prophylaxis and development of maternal vaccine options against the most implicated pathogens. Methods The PROGRESS study was an observational cohort study undertaken in Kampala, Uganda, between November 2018 and April 2021. If a woman delivered a stillborn baby, consent was sought for the collection of a heart-blood aspirate. One to three mL of blood was collected and sent for culture using the BD Bactec blood culture system. Organism identification was performed using biochemical testing and matrix-assisted laser desorption/ionization–time of flight mass spectrometry. Susceptibilities to appropriate panels of antimicrobials were determined by agar dilution. Results Kawempe Hospital registered 34 517 births in the study period, of which 1717 (5.0%) were stillbirths. A total of 581 (33.8%) were recruited into the study, and heart blood aspirates were performed on 569 (97.9%). Blood samples were sufficient for analysis of 476, with a total of 108 positive cultures (22.7% of sampled stillbirths). Fifty-nine of 108 blood cultures contained organisms that were considered potential pathogens, giving a pathogen positivity rate of 12.4%. Common pathogens included Enterococcus spp. (n = 14), Escherichia coli (n = 13), viridans streptococci (n = 18), Klebsiella pneumoniae (n = 6), and group B Streptococcus (n = 5). Gram-negative organisms were frequently resistant to commonly used first-line antimicrobials. Conclusions The high proportion of stillbirths caused by likely pathogenic bacteria in Uganda highlights the potential for prevention with prophylaxis and stresses the need for further investment in this area.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".