Effectiveness of the Modified WHO Labour Care Guide to Detect Prolonged and Obstructed Labour Among Women Admitted at Eight Publicly Funded, Midwife-Led Community Health Facilities in Rural Mbarara District, Southwestern Uganda: An Ambispective Cohort Study
Bibliographic record
Abstract
Godfrey R Mugyenyi,1,2 Wilson Tumuhimbise,3 Esther C Atukunda,2,4 Leevan Tibaijuka,1 Joseph Ngonzi,1 Musa Kayondo,1 Micheal Kanyesigye,2 Angella Musimenta,3 Fajardo T Yarine,1 Josaphat K Byamugisha5 1Obstetrics and Gynaecology Department, Mbarara University of Science and Technology, Mbarara, Uganda; 2Support Mom’s Project, Mbarara University of Science and Technology, Mbarara, Uganda; 3Computing and Informatics Department, Mbarara University of Science and Technology, Mbarara, Uganda; 4Pharmacy department, Mbarara University of Science and Technology, Mbarara, Uganda; 5Obstetrics and Gynaecology department, Makerere University College of Health Sciences, Kampala, UgandaCorrespondence: Godfrey R Mugyenyi, Email gmugyenyi@must.ac.ugBackground: Obstructed labour, a sequel of prolonged labour, remains a significant contributor to maternal and perinatal deaths in low resource settings.Objective: We evaluated the modified WHO labour care guide (LCG) in detecting prolonged/obstructed labour compared to the traditional partograph at publicly funded maternity centers in Southwestern Uganda.Methods: LCG was deployed to monitor labour by trained health care providers in 2023. We reviewed all patient labour monitoring records for the first quarter of 2024 (LCG-intervention) and 2023 (partograph-before LCG introduction) from eight randomized maternity centers. Our primary outcome was the proportion of women diagnosed with prolonged and or obstructed labour. Secondary outcomes included: mode of delivery, labour augmentation, stillbirths, maternal deaths, Apgar score, uterine rupture, postpartum haemorrhage and tool completion. Data was collected in REDcap and analyzed using STATA v17; statistical significance was p < 0.05.Results: A total of 991 (49.3%) and 1020 (50.7%) women were monitored using the LCG and partograph, respectively. The mean maternal and gestation ages were similar between the two groups, reported at 25.9 (SD=5.6) years, and 39.4 (SD=1.8) weeks, respectively. Overall, 120 (12.4%) cases of prolonged/obstructed labour were diagnosed (100 for LCG versus 20 for partograph); LCG had six times higher odds of diagnosing prolonged/obstructed labour compared to the partograph (aOR = 5.94;CI 95%3.63– 9.73, P < 0.001). Detection of obstructed labour alone using LCG increased 12-fold compared to the partograph (aOR = 11.74;CI 95%3.55– 38.74, P < 0.001). We observed increased Caesarean section rates (aOR=6.12;CI 95%4.32– 8.67, P < 0.001), augmentation of labour (aOR = 3.11;CI 95%1.81– 5.35, P < 0.001), Apgar Score at 5 minutes (aOR = 2.29;CI 95%1.11– 5.77, P = 0.025) and tool completion rate (aOR = 2.11;CI 95%1.08– 5.44, P < 0.001). We observed no differences in stillbirths, maternal deaths, postpartum haemorrhage and uterine rupture.Conclusion: Our data shows that LCG diagnosed more cases of prolonged and obstructed labour compared to the partograph among women delivering at rural publicly funded midwife-led facilities in Southwestern Uganda. More controlled and powered studies should evaluate the two tools in different facilities and sub-populations.Trial Registration: This trial registration was registered with clinical trials.gov number NCT05979194 on 2023-08-07, and the protocol was published by BMJ open, as 10.1136/bmjopen-2023-079216 on 15 April 2024.21 Trial registration number NCT05979194 clinical trials.gov.Keywords: modified WHO LCG, partograph, effectiveness, labour monitoring, ambispective cohort study, Uganda
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".