Bibliographic record
Abstract
Collecting useful data on a sufficiently large cohort of pregnancies in women with rheumatic disease is a challenge. The original manuscripts that demonstrated the dangers of pregnancy in women with lupus were relatively small case series. As larger prospective cohorts were collected by university-based experts, however, greater safety was demonstrated and the current norms of treatment were determined. In recent years, larger administrative databases have been tapped to study pregnancies not managed within university clinics and to study the long-term impact of maternal rheumatic disease on the offspring. Each of these methods of study has both strengths and weaknesses, adding a unique piece of data to our overall knowledge. We will discuss a range of approaches to the study of rheumatic disease in pregnancy, covering the potential benefits that each brings as well as the biases that can impact study results. When the results of studies are viewed through these lenses, each can contribute to our larger understanding of the rheumatic diseases in pregnancy.
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 | 0.155 | 0.615 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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".