Bibliographic record
Abstract
Several definitions of quality of health care have been used both in relation to health care and health systems. Quality in health care is defined as doing the right thing (getting the health care services needed), at the right time, in the right way to achieve the best possible results. The Institute of Medicine defines health care quality as “the degree to which health care services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”1 Because inflammatory bowel disease (IBD) is a chronic health condition with a typical course of remissions and relapses, defining and implementing quality of care indicators and outcomes are crucial for patients with IBD especially with significant variations of care given across different IBD centers.2 Developing evidence-based quality indicators could be a challenging process but a much more challenging task is implementing those indicators in routine clinical practice. The majority of the current quality indicators in IBD care are based on consensus agreements and expert opinions. Clinical quality indicators are quantitative endpoints used to guide, monitor, and improve the quality of patient care.3The ImproveCareNow (ICN) pediatric network demonstrated quality improvement by learning how to apply quality improvement methods to improve the care of pediatric patients with IBD, a model that has been adopted by other practitioners.4,5 Several outcome measures have developed including those of The American Gastroenterological Association and Crohn's and Colitis Foundation of America.3,6 The Emerging Practice in IBD Collaborative (EPIC) group generated 11 quality indicators for best-practice management of IBD in Canada.7 These indicators included pharmacological prophylaxis against deep vein thrombosis for hospitalized patients with acute IBD, testing for Clostridium difficile for these patients, counseling to improve smoking cessation for patients with Crohn's disease, proper documentation of diagnosis (Crohn's disease versus ulcerative colitis [UC]); disease location; and disease severity during initial diagnostic colonoscopy, using steroid-sparing agents in corticosteroid-dependent IBD, screening for tuberculosis and hepatitis B before initiating tumor necrosis factor antagonists, implementing rescue therapy for patients with acute severe UC after a maximum period of 7 days on intravenous corticosteroids, assessment and treatment for bone loss in high-risk patients for metabolic bone disease, screening for dysplasia in patients with colonic IBD with annual screening in those with concomitant primary sclerosing cholangitis, objective assessment for disease recurrence 6 to 12 months postsurgery in those who underwent intestinal resection for Crohn's disease, and recommending pneumococcal and annual influenza vaccination for patients with IBD especially for those on immunosuppression.7
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| 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".