2020 American College of Rheumatology Guideline for the Management of Reproductive Health in Rheumatic and Musculoskeletal Diseases
Bibliographic record
Abstract
OBJECTIVE: To develop an evidence-based guideline on contraception, assisted reproductive technologies (ART), fertility preservation with gonadotoxic therapy, use of menopausal hormone replacement therapy (HRT), pregnancy assessment and management, and medication use in patients with rheumatic and musculoskeletal disease (RMD). METHODS: We conducted a systematic review of evidence relating to contraception, ART, fertility preservation, HRT, pregnancy and lactation, and medication use in RMD populations, using Grading of Recommendations Assessment, Development and Evaluation methodology to rate the quality of evidence and a group consensus process to determine final recommendations and grade their strength (conditional or strong). Good practice statements were agreed upon when indirect evidence was sufficiently compelling that a formal vote was unnecessary. RESULTS: This American College of Rheumatology guideline provides 12 ungraded good practice statements and 131 graded recommendations for reproductive health care in RMD patients. These recommendations are intended to guide care for all patients with RMD, except where indicated as being specific for patients with systemic lupus erythematosus, those positive for antiphospholipid antibody, and/or those positive for anti-Ro/SSA and/or anti-La/SSB antibodies. Recommendations and good practice statements support several guiding principles: use of safe and effective contraception to prevent unplanned pregnancy, pre-pregnancy counseling to encourage conception during periods of disease quiescence and while receiving pregnancy-compatible medications, and ongoing physician-patient discussion with obstetrics/gynecology collaboration for all reproductive health issues, given the overall low level of available evidence that relates specifically to RMD. CONCLUSION: This guideline provides evidence-based recommendations developed and reviewed by panels of experts and RMD patients. Many recommendations are conditional, reflecting a lack of data or low-level data. We intend that this guideline be used to inform a shared decision-making process between patients and their physicians on issues related to reproductive health that incorporates patients' values, preferences, and comorbidities.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".