Recurring red flags: a retrospective study of MBRRACE-UK Perinatal Mortality Surveillance (2015–21) to identify maternity services most consistently reporting higher-than-average deaths
Bibliographic record
Abstract
BACKGROUND: This study aimed to identify hospital trusts in England most consistently reporting higher-than-average rates of extended perinatal mortality (EPM), including stillbirths and neonatal deaths. METHODS: We conducted a retrospective study of MBRRACE-UK Perinatal Mortality Surveillance Reports (2015-21) comparing EPM rates for births occurring in 124 hospital trusts in England between 2013 and 2019. Utilizing MBRRACE-UK definitions and designations, including coloured bands (red and amber indicate higher death rates), we devised a scoring method to determine which trusts most consistently reported higher-than-average rates of EPM throughout seven years. RESULTS: We identified 23 (18.5% of 124) 'red flag' trusts most consistently falling into MBRRACE-UK red and amber bands. They included Shrewsbury and Telford Hospitals NHS Trust (SaTH) and East Kent Hospitals University Trust, both under investigation during the parliamentary Health and Social Care Committee's inquiry into the safety of maternity services in England. Seven trusts, including SaTH, reported higher-than-average deaths in all seven years. Indications of regional patterns were evident. CONCLUSIONS: By examining maternity services mortality data over an extended period, patterns of clinical significance may emerge. We found evidence of a minority of trusts in England consistently reporting higher-than-average rates of EPM. These red flags may warrant further attention.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".