Alberta Collaborative Quality Improvement Strategies to Improve Outcomes of Moderate and Late Preterm Infants (ABC-QI) Trial: a protocol for a multicentre, stepped-wedge cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Evidence-based Practice for Improving Quality (EPIQ) is a collaborative quality improvement method adopted by the Canadian Neonatal Network that led to decreased mortality and morbidity in very preterm neonates. The Alberta Collaborative Quality Improvement Strategies to Improve Outcomes of Moderate and Late Preterm Infants (ABC-QI) Trial aims to evaluate the impact of EPIQ collaborative quality improvement strategies in moderate and late preterm neonates in Alberta, Canada. METHODS: In a 4-year, multicentre, stepped-wedge cluster randomized trial involving 12 neonatal intensive care units (NICUs), we will collect baseline data with the current practices in the first year (all NICUs in the control arm). Four NICUs will transition to the intervention arm at the end of each year, with 1 year of follow-up after the last group transitions to the intervention arm. Neonates born at 32 + 0 to 36 + 6 weeks' gestation with primary admission to NICUs or postpartum units will be included. The intervention includes implementation of respiratory and nutritional care bundles using EPIQ strategies, including quality improvement team building, quality improvement education, bundle implementation, quality improvement mentoring and collaborative networking. The primary outcome is length of hospital stay; secondary outcomes include health care costs and short-term clinical outcomes. Neonatal intensive care unit staff will complete a survey in the first year to assess quality improvement culture in each unit, and a sample will be interviewed 1 year after implementation in each unit to evaluate the implementation process. INTERPRETATION: The ABC-QI Trial will assess whether collaborative quality improvement strategies affect length of stay in moderate and late preterm neonates. It will provide detailed population-based data to support future research, benchmarking and quality improvement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT05231200.
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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 | 0.047 | 0.043 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".