Advances in the diagnosis and management of inflammatory bowel disease: Challenges and uncertainties
Bibliographic record
Abstract
Over the past two decades, several advances have been made in the management of patients with inflammatory bowel disease (IBD) from both evaluative and therapeutic perspectives. This review discusses the medical advancements that have recently been made as the standard of care for managing patients with ulcerative colitis (UC) and Crohn's Disease (CD) and to identify the challenges associated with implementing their use in clinical practice. A comprehensive literature search of the major databases (PubMed and Embase) was conducted for all recent scientific papers (1990-2013) giving the recent updates on the management of IBD and the data were extracted. The reported advancements in managing IBD range from diagnostic and evaluative tools, such as genetic tests, biochemical surrogate markers of activity, endoscopic techniques, and radiological modalities, to therapeutic advances, which encompass medical, endoscopic, and surgical interventions. There are limited studies addressing the cost-effectiveness and the impact that these advances have had on medical practice. The majority of the advances developed for managing IBD, while considered instrumental by some IBD experts in improving patient care, have questionable applications due to constraints of cost, lack of availability, and most importantly, insufficient evidence that supports their role in improving important long-term health-related outcomes.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".