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Record W2910353451 · doi:10.21037/tp.2019.01.02

Quality improvement in healthcare for patients with inflammatory bowel disease

2019· review· en· W2910353451 on OpenAlexaffabout
Yasser Negm, Wael El‐Matary

Bibliographic record

VenueTranslational Pediatrics · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsChildren's Hospital of Winnipeg
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseQuality managementQuality (philosophy)Health careDiseaseIntensive care medicineCrohn's diseaseUlcerative colitisQuality of life (healthcare)Family medicineInternal medicineNursingOperations managementManagement system

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.311
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2019
Admission routes2
Has abstractyes

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