Optimizing maternal and neonatal outcomes through tight control management of inflammatory bowel disease during pregnancy: a pilot feasibility study
Bibliographic record
Abstract
A home point-of care FCP test (IBDoc) and a self-reported clinical disease activity program (IBD Dashboard) may improve routine monitoring of IBD activity during pregnancy. We aimed to evaluate the feasibility of tight control management using remote monitoring in pregnant patients with IBD. Pregnant patients (< 20 weeks) with IBD were prospectively enrolled from Mount Sinai Hospital between 2019 and 2020. Patients completed the IBDoc and IBD Dashboard at three core time points. Disease activity was measured clinically using the Harvey-Bradshaw Index (mHBI) for CD and partial Mayo (pMayo) for UC, or objectively using FCP. A feasibility questionnaire was completed in the third trimester. Seventy-seven percent of patients (24 of 31) completed the IBDoc and IBD Dashboard at all core time points. Twenty-four patients completed the feasibility questionnaires. All survey respondents strongly preferred using the IBDoc over standard lab-based testing and would use the home kit in the future. Exploratory analysis identified discordance rates of more than 50% between clinical and objective disease activity. Tight control management using remote monitoring may be feasible among pregnant patients with IBD. A combination of both clinical scores and objective disease markers may better predict disease activity.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".