A Quality Improvement Bundle to Improve Informed Choice for Children With Typical, Newly Diagnosed Immune Thrombocytopenia
Bibliographic record
Abstract
IVIG has been the predominant therapy for the initial management of children with newly diagnosed immune thrombocytopenia at our hospital. With current guidelines supporting more conservative management, we undertook a quality improvement initiative to lead practice change. Over a 2-year time period (2013 to 2015), we strove to decrease use of hospital resources (use of IVIG, length of stay) while optimizing family satisfaction. An interdisciplinary working group was struck and a quality improvement bundle was implemented. The bundle comprised a patient information sheet; an evidence-informed, consensus-based protocol; and promotion of shared decision-making via stakeholder engagement and education. Data were collected prospectively; baseline data from a 2007 to 2009 audit were used for comparison. In total, 27 patients were included. Mean initial platelet count was 4×10/L. Bleeding was classified as none or mild in 56% of patients. IVIG use decreased from 88% to 55% of patients, corticosteroid prescription increased from 6% to 15%, and observation increased from 6% to 30% of patients. Hospital length of stay decreased from 47 to 36 hours. Family satisfaction was stable across treatment groups. Through introduction of a quality improvement initiative, we were able to improve family-centered care and decrease use of hospital resources.
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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 | 0.104 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".