Quality improvement in healthcare for patients with inflammatory bowel disease
Bibliographic record
Abstract
Since inflammatory bowel diseases (IBD) are chronic disorders with typical remission and relapses and no cure, maintaining high quality of provided healthcare to patients with IBD plays a major role in the management and reducing disease-related morbidity. To hone process-based quality indicators in order to ameliorate quality of care, the indicators must be based on high quality evidence and expert consensus. ImproveCareNow (ICN) group gave us a great example of quality improvement (QI) by gaining experience in how to exercise, apply and implement QI methods in the care of children with IBD. "The American Gastroenterological Association" has developed an adult "IBD physician performance measures set" and both "Crohn's and Colitis Foundation of America" (CCFA) and "Crohn's and Colitis Canada" (CCC) have developed sets of most highly rated process and outcome measures. "The Emerging Practice in IBD Collaborative (EPIC) Canadian group" developed definitions of quality indicators for best-practice management of IBD in Canada. "Quality of Care through the Patient's Eyes (QUOTE-IBD)" was honed as a questionnaire to quantify quality of care in the eyes of patients with IBD. This is now widely used in several European countries. The current concepts of quality of care as well as quality indicators will be discussed in this article.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".