Pregnancy: Data, Outcomes, and Treatment Paradigms in Rheumatology
Bibliographic record
Abstract
“Doctor, I’m pregnant.” Coming from a patient with rheumatoid arthritis (RA), or another systemic inflammatory autoimmune disease, who is taking a handful of medications, these few words reliably quicken the pulse of all rheumatologists. Perhaps the only thing more worrisome is when the patient says “Doctor, I want to become pregnant.” In either instance, the apprehensive rheumatologist has to weigh the patient’s wishes against the uncertainties inherent in drug use or cessation, and the effects of either on both mother and fetus. The physician’s counsel may range from the pure “stop all your rheumatologic medications”; to the positive “wait and the pregnancy will improve your RA”; or the pragmatic “go see a high-risk obstetrician immediately.” Often our meager guidance is “let me know when things don’t go as planned” or “call me when you’re done.” The patient who wishes to become pregnant is in a difficult situation. It involves actual discussion between doctor and patient without a solid plan, in an area with a dearth of clinical data from which reasonable decisions could be made. Traditionally, such clinical discussions tended to be as dissatisfying to doctors as they were to patients. Pregnancy has often been treated as if it were an adverse event. It was considered something that interrupted an otherwise carefully planned treatment course for the patient’s rheumatologic conditions. Currently, the goal is remission, achieved by using the best possible treatment approaches. The goals of therapy may well be incongruous with the plan for a … Address correspondence to Dr. J.J. Cush, Baylor Research Institute, 9900 N. Central Expressway, Suite 550, Dallas, Texas 75231, USA. E-mail: jjcush{at}gmail.com
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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 | 0.194 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".