Childbearing with Hypermobile Ehlers–Danlos Syndrome and Hypermobility Spectrum Disorders: A Large International Survey of Outcomes and Complications
Bibliographic record
Abstract
One in 20 births could be affected by hypermobile Ehlers–Danlos syndrome or Hypermobility Spectrum Disorders (hEDS/HSD); however, these are under-diagnosed and lacking research. This study aimed to examine outcomes and complications in people childbearing with hEDS/HSD. A large online international survey was completed by women with experience in childbearing and a diagnosis of hEDS/HSD (N = 947, total pregnancies = 1338). Data were collected on demographics, pregnancy and birth outcomes and complications. Participants reported pregnancies in the UK (N = 771), USA (N = 364), Australia (N = 106), Canada (N = 60), New Zealand (N = 23) and Ireland (N = 14). Incidences were higher in people with hEDS/HSD than typically found in the general population for pre-eclampsia, eclampsia, pre-term rupture of membranes, pre-term birth, antepartum haemorrhage, postpartum haemorrhage, hyperemesis gravidarum, shoulder dystocia, caesarean wound infection, postpartum psychosis, post-traumatic stress disorder, precipitate labour and being born before arrival at place of birth. This potential for increased risk related to maternal and neonatal outcomes and complications highlights the importance of diagnosis and appropriate care considerations for childbearing people with hEDS/HSD. Recommendations include updating healthcare guidance to include awareness of these possible complications and outcomes and including hEDS/HSD in initial screening questionnaires of perinatal care to ensure appropriate consultation and monitoring can take place from the start.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".