Quality of care in inflammatory bowel diseases: What is the best way to better outcomes?
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a lifelong, progressive disease that has disabling impacts on patient's lives. Given the complex nature of the diagnosis of IBD and its management there is consequently a large economic burden seen across all health care systems. Quality indicators (QI) have been created to assess the different façades of disease management including structure, process and outcome components. Their development serves to provide a means to target and measure quality of care (QoC). Multiple different QI sets have been published in IBD, but all serve the same purpose of trying to achieve a standard of care that can be attained on a national and international level, since there is still a major variation in clinical practice. There have been many recent innovative developments that aim to improve QoC in IBD including telemedicine, home biomarker assessment and rapid access clinics. These are some of the novel advancements that have been shown to have great potential at improving QoC, while offloading some of the burden that IBD can have vis-a-vis emergency room visits and hospital admissions. The aim of the current review is to summarize and discuss available QI sets and recent developments in IBD care including telemedicine, and to give insight into how the utilization of these tools could benefit the QoC of IBD patients. Additionally, a treating-to-target structure as well as evidence surrounding aggressive management directed at tighter disease control will be presented.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".