Sustained increase in annual transcranial Doppler screening rates in children with sickle cell disease: A quality improvement project
Bibliographic record
Abstract
INTRODUCTION: Individuals with sickle cell disease (SCD) at increased risk for stroke should undergo annual stroke risk assessment using transcranial Doppler (TCD) screening between the ages of 2 and 16. Though this screening can significantly reduce morbidity associated with SCD, screening rates at Boston Children's Hospital (and nationwide) remain below the recommended 100% screening adherence rates. METHODS: Three plan-do-study-act (PDSA) cycles were designed and implemented. The Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART) aim of our quality improvement (QI) initiative was to sustainably increase the proportion of eligible patients receiving a TCD within 15 months of their last TCD to greater than 95%. An interrupted time series (ITS) analysis was performed, comparing TCD adherence rates from PDSA Cycle 1 to those from PDSA Cycles 2 and 3. RESULTS: Mean TCD adherence increased across all three PDSA cycles, from a baseline of 67% in the first cycle (January 2015 to September 2020) to 92% in the third cycle (May 2021 to March 2023). In the ITS analysis of TCD adherence rates, there was a significant difference in the final TCD adherence rate achieved compared to the rate predicted, with a total estimated increase in adherence of 17.9% being attributable to the interventions from PDSA Cycles 2 and 3. DISCUSSION: Although other QI initiatives had demonstrated ability to increase adherence to TCD screening for patients with SCD, this is the first QI project to collect data over such a prolonged period of time to demonstrate a sustained increase in screening rates throughout the intervention (an 8-year period).
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".