Haemoglobin levels in early pregnancy and severe maternal morbidity: population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: Data regarding low maternal haemoglobin concentration and severe maternal morbidity (SMM) are limited and potentially biased. This study evaluated the relation between early maternal haemoglobin concentration and SMM or maternal mortality. DESIGN: Population-based cohort study. SETTING: Ontario, Canada, in a public healthcare system. POPULATION: 737 393 births with a routine outpatient haemoglobin measured at a calculated gestational age of 2-16 weeks. METHODS: The relation between early-pregnancy outpatient blood haemoglobin concentration and each study outcome was expressed as adjusted relative risks (aRR) and absolute risk differences (aRD), with 95% confidence intervals (CI), generated by modified Poisson regression. MAIN OUTCOME MEASURES: The primary outcome was SMM or maternal mortality, from 23 weeks' gestation to 42 days postpartum. RESULTS: The mean (SD) haemoglobin concentration was 126.9 (9.3) g/l. Overall, SMM or death occurred in 13 514 pregnancies (1.8%). Relative to a haemoglobin level of 125-129 g/l, the aRR was 1.07 (95% CI 1.02-1.13) and aRD (0.09%, 95% CI 0.01-0.18) at 120-124 g/l; aRR 1.31 (95% CI 1.17-1.46) and aRD 0.47% (95% CI 0.24-0.69) at 105-109 g/l; and aRR 4.53 (95% CI 3.59-5.72) and aRD 5.94% (95% CI 4.12-7.76) at <90 g/l. In all, 5961 women (0.8%) required red cell transfusion, with significantly higher risks at all haemoglobin concentrations below 125-129 g/l, peaking at a haemoglobin level <90 g/l (aRR 11.82, 95% CI 9.30-15.03). CONCLUSION: There is a gradual increase in the risk of SMM or death, as well as red cell transfusion, starting from the lower level of the normal range of haemoglobin of non-pregnant women. TWEETABLE ABSTRACT: Women with low haemoglobin in early pregnancy are at higher future risk of morbidity, death and blood transfusion.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".