Underreporting of maternal and neonatal complications: A comparison of information in maternity registers and client charts at a rural community hospital in Malawi
Bibliographic record
Abstract
To determine the rate and types of unreported maternal and neonatal complications in a rural community hospital in Malawi. The problem of maternal and neonatal morbidity and mortality may be underestimated, with underreporting of complications often noted. Reliable data is needed to make key decisions at the local, district, and national level. This study investigated whether there were unreported complications among women receiving intrapartum care at a rural community hospital in Malawi. A retrospective cross-sectional study was conducted comparing maternity register records to client charts from January-March 2018. Descriptive data analysis using SPSS v20 was performed to calculate percentages and frequencies. 360 client cases were identified, of which 33 cases were excluded from analysis due to missing charts. Of the remaining 327 cases included in the final analysis, only 34% (n = 31) of maternal and 34% (n = 33) of neonatal complications were recorded in both the maternity register and the chart. When the additional complications found in the chart review were included, the rates of maternal and neonatal complications tripled from 9.5% (n = 31) and 10% (n = 33) to 28% (n = 90) and 30% (n = 98), respectively. There was poor record keeping, underreporting of maternal and neonatal complications, and discrepancies between the data recorded in the monthly maternity register and client charts in the first quarter of 2018. The actual rate of complications suggests a need to verify data at the facility level to prevent release and reporting of inaccurate data. Measures are needed to mitigate the gaps in data reporting.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".