Postpartum haemorrhage trends in Sweden using the Robson ten group classification system: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine postpartum haemorrhage (PPH) trends in Sweden using the Robson classification system. DESIGN: Population-based cohort study. SETTING: Sweden. POPULATION: Deliveries in 2000-2016, classified as Robson groups 1-5 (singleton pregnancies in vertex presentation, from gestational weeks 37+0; n = 1 590 178). METHODS: We examined temporal trends in PPH between 2000 and 2016 overall, and within each Robson group, and performed logistic regression to examine the influence of changes in risk factors (maternal, comorbidity, obstetric practice and infant factors) over time. MAIN OUTCOME MEASURES: Postpartum haemorrhage, defined as an estimated blood loss of >1000 ml. RESULTS: The overall PPH rate increased from 5.4 to 7.3%, corresponding to a 37% (OR 1.37, 95% CI 1.32-1.42) increase over time. Rates varied between Robson groups, ranging from 4.5% in group 3 to 14.3% in group 4b. Increasing trends in PPH were found in all Robson groups except for groups 2b and 4b (prelabour caesarean deliveries). In the unstratified analysis, adjusting for maternal, comorbidity and obstetric practice factors slightly attenuated the risk of PPH in the later period (2013-2016), compared with the reference period (2000-2004; crude OR 1.26, 95% CI 1.24-1.29, adjusted OR 1.22, 95% CI 1.20-1.25). Within individual Robson groups, changes in risk factors did not explain increasing rates of PPH. CONCLUSIONS: Postpartum haemorrhage rates varied between Robson groups. Changes in risk factors could not explain the 37% increase in PPH for women in Robson groups 1-5 in Sweden, 2000-2016. TWEETABLE ABSTRACT: Changes in risk factors could not explain the increasing trend of PPH in Sweden, and rates of PPH varied widely between Robson groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".