Proceedings of the 2019 Childhood Arthritis and Rheumatology Research Alliance (CARRA) Annual Scientific Meeting
Bibliographic record
Abstract
Background : Cardiovascular disease occurs in 25 -60% of patients with childhood-onset systemic lupus erythematosus (cSLE) and up to 30% with mixed connective tissue disease (MCTD).The echocardiogram (ECHO) has been recommended as the most useful non-invasive method to assess cardiac involvement, especially early preclinical disease.Current best practice is to obtain an ECHO at diagnosis of cSLE or MCTD and annually, particularly if the baseline ECHO is abnormal.We observed a low frequency of ECHO screening in cSLE and MCTD patients at our rheumatology center and thus initiated a quality improvement project to improve the annual rate at our center, with the goal of reaching 80% in 12 months.Methods : We used a Plan-Do-Study-Act approach.We obtained baseline data from April to June 2017 and conducted a fishbone analysis of barriers to ECHO completion (Figure 1).Our pediatric rheumatologists, fellows and nursing team participated in this project.Our primary and process measures were tracked on individual (pediatric rheumatologists) and aggregate level.Our primary outcome measure was percentage of patients with annual ECHO within the last year of a clinic visit.Our process measures were (i) Physician ordering of ECHOs for patients without annual ECHOs per clinic visit (ii) Completion of ordered ECHO.Our balance measure was duration of clinic visit.Participant education was completed in 2/2018.We have completed three cycles (Figure 2).Our final cycle is in progress (12/2018 to 1/2019).Results : At baseline, 62% of our patients had ECHOs within a year prior to a clinic visit.In addition, only 20% of the patients without an ECHO within the last year at a clinic visit had an ECHO ordered at that clinic visit.The percentage of patients up to date on annual ECHOs at the time of a clinic visit at the end of cycles 1, 2 and 3 was 83%, 85% and 83% respectively (Figure 2 and 3).Physician ordering of ECHOs for patients without a yearly ECHO was 72%, 52% and 71% at the end of cycles 1, 2 and 3 respectively.Conclusions : Using a multidisciplinary team approach, we achieved and sustained our goal of increasing annual ECHO screening for our patients with cSLE and MCTD.IRB approval was not required for this quality improvement project.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.043 |
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".