The Incidence and Causes of Maternal Near Miss in a Pandemic at Georgetown Public Hospital Corporation
Bibliographic record
Abstract
Objective: This article determines the incidence, causes and socio-demographics factors of Maternal Near Miss at GPHC from June 1, 2021 to December 31, 2021. Design and Method: A retrospective descriptive chart review was conducted during a seven-month period from June 1st, 2021 to December 31st, 2021 of all the pregnant patients who had suffered a Maternal Near Miss. A purposive sampling technique was employed in this study. Eligible patients were identified using a modified World Health Organization Maternal Near Miss criterion that Guyana has implemented. The data was collected and analyzed in an Excel format. Results: There were a total of 4636 admissions during the study period with 2984 live births and 32 maternal near misses which accounted for 0.7% of all births. The Maternal Near Miss incidence ratio was 10.7 MNM/LB which indicates that for every 1000 live births there were 11 maternal near misses. There was a total of 9 maternal deaths that occurred during the study period. The Maternal Near Miss to Mortality Ratio was 32:9, resulting in a proportion of 3.5 MNM per MD. The primary causes of maternal near misses in the patients in this study were: obstetric hemorrhage, hypertensive disorder, ectopic or abortion and infection. Anemia was the main secondary cause of maternal near misses and ICU admissions. Conclusion: The maternal near miss ratio in this research was slightly lower (10.7MNM/LB) than the study conducted at GPHC in 2019, which recorded a maternal near miss of 12.7 MNM/LB. There was an increase in obstetrical hemorrhage and a decrease in Hypertensive disorders which were main indications of MNM. This study discovered that for every maternal death, 4 mothers were saved whereas the 2019 study revealed 5 mothers were saved. It can be concluded that MNM rates remained stable during the pandemic compared to pre-pandemic period. Keywords: Maternal Near Miss, Pandemic, Georgetown Public Hospital Corporation, GPHC
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".