Assessing patient-level risk factors for evidence-based early diagnosis of maternal sepsis
Bibliographic record
Abstract
BACKGROUND: Maternal sepsis is a leading cause of maternal death, with the burden higher in low- and middle-income countries (LMICs). Early Warning Systems (EWS) combine clinical observations to identify a pattern consistent with an increased risk of clinical deterioration and have been introduced for monitoring sepsis risk. Maternal sepsis risks in LMICs are driven by factors at the health system and patient levels. This study assessed patient-level risk factors -age, health-seeking behaviour, comorbidities and procedures- associated with maternal sepsis in an urban tertiary hospital in Nigeria. METHODS: We conducted a retrospective study using health records of 4,510 patients from obstetrics and gynaecology units at a tertiary hospital in southwestern Nigeria from 2016 to 2020. To examine the association between patient-level risk factors and sepsis, we analysed data for the 565 maternal patients with a record of infection using a multiple logistic regression model. We extended the model by introducing interaction terms to assess whether the association between the risk factors and maternal sepsis varied by socio-demographic factors. RESULTS: About one-fifth of the 565 maternal patients with an infection had sepsis. Patients with sepsis had the lowest rate of live birth (29.7%) compared to those with (41.8%) and without (82.1%) an infection. Proportions of stillbirth (intrauterine fetal death) and early neonatal deaths were highest among patients with sepsis (15.3% and 1.8%) compared to those with (13.2% and 2.1%) and without (4.5% and 1.7%) an infection. Antenatal care booking status (OR: 0.17; 95% CI: 0.08-0.38) and having a catheter (OR: 2.60; 95% CI: 1.35-5.01) were significantly associated with maternal sepsis in the adjusted model. CONCLUSION: Our results suggest that improving access to antenatal care services for pregnant women will substantially reduce the risk of maternal sepsis in the Nigerian population. Guidelines for maternal sepsis management should consider subgroups of patients at higher risk, such as those with urethral catheters.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".