Validation of community health worker identification of maternal puerperal sepsis using a clinical diagnostic algorithm in Bangladesh and Pakistan
Bibliographic record
Abstract
Puerperal sepsis (PP sepsis) is a leading cause of maternal mortality globally. The majority of maternal sepsis cases and deaths occur at home and remain undiagnosed and under-reported. In this paper, we present findings from a nested case-control study in Bangladesh and Pakistan which sought to assess the validity of community health worker (CHW) identification of PP sepsis using a clinical diagnostic algorithm with physician assessment and classification used as the gold standard.Up to 300 postpartum women were enrolled in each of the 3 sites 1) Sylhet, Bangladesh (n = 278), 2) Karachi, Pakistan (n = 278) and 3) Matiari, Pakistan (n = 300). Index cases were women with suspected PP Sepsis as diagnosed by CHWs clinical assessment of one or more of the following signs and symptoms: temperature (recorded fever ≥38.1°C, reported history of fever, lower abdominal or pelvic pain, and abnormal or foul-smelling discharge. Each case was matched with 3 control women who were diagnosed by CHWs to have no infection. Cases and controls were assessed by trained physicians using the same algorithm implemented by the CHWs. Using physician assessment as the gold standard, Kappa statistics for reliability and diagnostic validity (sensitivity and specificity) are presented with 95% CI. Sensitivity and specificity were adjusted for verification bias.The adjusted sensitivity and specificity of CHW identification of PP sepsis across all sites was 82% (Karachi: 78%, Matiari: 78%, Sylhet: 95%) and 90% (Karachi: 95%, Matiari: 85%, Sylhet: 90%) respectively. CHW-Physician agreement was highest for moderate and high fever (range across sites: K = 0.84-0.97) and lowest for lower abdominal pain (K = 0.30-0.34). The clinical signs and symptoms for other conditions were reported infrequently, however, the CHW-physician agreement was high for all symptoms except severe headache/ blurred vision (K = 0.13-0.38) and reported "lower abdominal pain without fever" (K = 0.39-0.57).In all sites, CHWs with limited training were able to identify signs and symptoms and to classify cases of PP sepsis with high validity. Integrating postpartum infection screening into existing community-based platforms and post-natal visits is a promising strategy to monitor women for PP sepsis - improving delivery of cohesive maternal and child health care in low resource settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".