Evaluating the effectiveness of a multifaceted intervention to reduce low-value care in adults hospitalized following trauma: a protocol for a pragmatic cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: While simple Audit & Feedback (A&F) has shown modest effectiveness in reducing low-value care, there is a knowledge gap on the effectiveness of multifaceted interventions to support de-implementation efforts. Given the need to make rapid decisions in a context of multiple diagnostic and therapeutic options, trauma is a high-risk setting for low-value care. Furthermore, trauma systems are a favorable setting for de-implementation interventions as they have quality improvement teams with medical leadership, routinely collected clinical data, and performance-linked to accreditation. We aim to evaluate the effectiveness of a multifaceted intervention for reducing low-value clinical practices in acute adult trauma care. METHODS: We will conduct a pragmatic cluster randomized controlled trial (cRCT) embedded in a Canadian provincial quality assurance program. Level I-III trauma centers (n = 30) will be randomized (1:1) to receive simple A&F (control) or a multifaceted intervention (intervention). The intervention, developed using extensive background work and UK Medical Research Council guidelines, includes an A&F report, educational meetings, and facilitation visits. The primary outcome will be the use of low-value initial diagnostic imaging, assessed at the patient level using routinely collected trauma registry data. Secondary outcomes will be low-value specialist consultation, low-value repeat imaging after a patient transfer, unintended consequences, determinants for successful implementation, and incremental cost-effectiveness ratios. DISCUSSION: On completion of the cRCT, if the intervention is effective and cost-effective, the multifaceted intervention will be integrated into trauma systems across Canada. Medium and long-term benefits may include a reduction in adverse events for patients and an increase in resource availability. The proposed intervention targets a problem identified by stakeholders, is based on extensive background work, was developed using a partnership approach, is low-cost, and is linked to accreditation. There will be no attrition, identification, or recruitment bias as the intervention is mandatory in line with trauma center designation requirements, and all outcomes will be assessed with routinely collected data. However, investigators cannot be blinded to group allocation and there is a possibility of contamination bias that will be minimized by conducting intervention refinement only with participants in the intervention arm. TRIAL REGISTRATION: This protocol has been registered on ClinicalTrials.gov (February 24, 2023, # NCT05744154 ).
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 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 | 0.080 | 0.087 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.057 | 0.008 |
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".