Patient Care Pathways for Pregnancy in Rare and Complex Rheumatic Diseases: Results From an International Survey
Bibliographic record
Abstract
OBJECTIVE: To map existing organizational care pathways in clinical centers of expertise that care for pregnant women affected by rare and complex connective tissue diseases (rcCTDs). METHODS: An international working group composed of experts in the field of pregnancy in rcCTDs co-designed a survey focused on organizational aspects related to the patient's pathway before, during, and after pregnancy. The survey was distributed to subject experts through referral sampling. RESULTS: Answers were collected from 69 centers in 21 countries. Patients with systemic lupus erythematosus and/or antiphospholipid syndrome were followed by more than 90% of centers, whereas those with disorders such as IgG4-related diseases were rarely covered. In the majority of centers, a multidisciplinary team was involved, including an obstetrician/gynecologist in 91.3% of cases and other healthcare professionals less frequently. Respondents indicated that 96% of the centers provided routine pre-pregnancy care, whereas the number of patient visits during pregnancy varied across centers. A formalized care pathway was described in 49.2% of centers, and 20.3% of centers had a predefined protocol for the monitoring of pregnant patients. Access to therapies during pregnancy also was heterogeneous among different centers. CONCLUSION: In international referral centers, a high level of care is provided to patients with rcCTDs before, during, and after pregnancy. No significant discrepancies were found between European and non-European countries. However, this work highlights a potential benefit to streamlining the care approaches across countries to optimize pregnancy and perinatal outcomes among patients with rcCTDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".