Severe maternal morbidity surveillance, temporal trends and regional variation: A population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To quantify temporal trends and regional variation in severe maternal morbidity (SMM) in Sweden. DESIGN: Cohort study. POPULATION: Live birth and stillbirth deliveries in Sweden, 1999-2019. METHODS: Types and subtypes of SMM were identified, based on a standard list (modified for Swedish clinical setting after considering the frequency and validity of each indicator) using diagnoses and procedure codes, among all deliveries at ≥22 weeks of gestation (including complications within 42 days of delivery). Contrasts between regions were quantified using rate ratios (RRs) and 95% confidence intervals (95% CIs). Temporal changes in SMM types and subtypes were described. MAIN OUTCOME MEASURES: Types and subtypes of SMM. RESULTS: There were 59 789 SMM cases among 2 212 576 deliveries, corresponding to 270.2 (95% CI 268.1-272.4) per 10 000 deliveries. Composite SMM rates increased from 236.6 per 10 000 deliveries in 1999 to 307.3 per 10 000 deliveries in 2006, before declining to 253.8 per 10 000 deliveries in 2019. Changes in composite SMM corresponded with temporal changes in severe haemorrhage rates, which increased from 94.9 per 10 000 deliveries in 1999 to 169.3 per 10 000 deliveries in 2006, before declining to 111.2 per 10 000 deliveries in 2019. Severe pre-eclampsia, eclampsia and HELLP (haemolysis, elevated liver enzymes and low platelet count) syndrome (103.8 per 10 000 deliveries), severe haemorrhage (133.7 per 10 000 deliveries), sepsis, embolism, disseminated intravascular coagulation, shock and severe mental health disorders were the most common SMM types. Rates of embolism, disseminated intravascular coagulation and shock, acute renal failure, cardiac complications, sepsis and assisted ventilation increased, whereas rates of surgical complications, severe uterine rupture and anaesthesia complications declined. CONCLUSIONS: The observed spatiotemporal variations in composite SMM and SMM types provide substantive insights and highlight regional priorities for improving maternal health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".