Noninvasive Methods For Assessing Inflammatory Bowel Disease Activity in Pregnancy
Bibliographic record
Abstract
Active inflammatory bowel disease (IBD) may increase the risk of adverse outcomes during pregnancy. Our aim was to systematically review the role of noninvasive fecal tests, such as fecal calprotectin (FCP) and lactoferrin (FL), and laboratory tests including C-reactive protein (CRP), hemoglobin, and albumin in the assessment of IBD during pregnancy. A systematic search of electronic databases was performed through October 2018 for studies assessing the utility of fecal and laboratory tests in predicting IBD activity in pregnant patients. Active disease was defined based on routinely used clinical criteria such as the Harvey-Bradshaw Index or Mayo score for ulcerative colitis. Noninvasive test levels were stratified by the presence of active disease and by gestational period (preconception, first trimester, second trimester, and third trimester). Thirteen studies were included. Both FCP and FL levels were significantly higher in pregnant patients with IBD compared with those without IBD. FCP levels were also significantly higher in patients with active disease compared with those with the inactive disease during all gestational periods. Furthermore, 3 studies demonstrated no consistent correlation with serum CRP and active IBD during pregnancy. Similarly, serum albumin and hemoglobin levels did not correlate with disease activity in pregnant patients with IBD. Given the lack of high-quality evidence, only FCP appears to correlate with IBD activity in all gestational periods of pregnancy. The utility of the other noninvasive tests such as serum CRP, hemoglobin, and albumin remains to be determined in this population.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".