Development of a Global Rating Scale for Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: The Global Rating Scale (GRS) is a web-based self-assessment quality improvement tool used to identify gaps in health care, change the focus to patient-centred care and standardize care. There are four levels of achievement ranging from basic-(D) to excellent-(A) service delivery. The goal was to develop a GRS for inflammatory bowel disease (IBD) to improve the quality of care for patients on a system level. METHODS: The IBD GRS was developed through an iterative process and modeled upon the successful endoscopy GRS programs in the United Kingdom and Canada. Dimensions, items and statements were drafted based on expert opinions, patient-informed quality indicators and best available evidence, then reviewed and modified by a core committee. A working group of IBD and GRS experts voted in-person to establish consensus on the inclusion and quality of statements. RESULTS: Two dimensions (Clinical Quality and Quality of Patient Experience), 10 items and 89 statements made up the IBD GRS. There was a 100% response rate for each of the 40 votes for statements in the IBD GRS. All statements within each level received a mean rating score between four (agree) and five (strongly agree). Revisions agreed upon during the voting process were incorporated into the IBD GRS. Group consensus was achieved on the inclusion of statements, and 10 items were selected as standards within the two dimensions. CONCLUSIONS: We have developed the first IBD GRS with the aim of improving quality of care through ongoing evaluations and improvements by health care teams, focusing on patient-centred care.
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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".