The evaluation of enhanced feedback interventions to reduce unnecessary blood transfusions (AFFINITIE): protocol for two linked cluster randomised factorial controlled trials
Bibliographic record
Abstract
BACKGROUND: Blood for transfusion is a frequently used clinical intervention, and is also a costly and limited resource with risks. Many transfusions are given to stable and non-bleeding patients despite no clear evidence of benefit from clinical studies. Audit and feedback (A&F) is widely used to improve the quality of healthcare, including appropriate use of blood. However, its effects are often inconsistent, indicating the need for coordinated research including more head-to-head trials comparing different ways of delivering feedback. A programmatic series of research projects, termed the 'Audit and Feedback INterventions to Increase evidence-based Transfusion practIcE' (AFFINITIE) programme, aims to test different ways of developing and delivering feedback within an existing national audit structure. METHODS: The evaluation will comprise two linked 2×2 factorial, cross-sectional cluster-randomised controlled trials. Each trial will estimate the effects of two feedback interventions, 'enhanced content' and 'enhanced follow-on support', designed in earlier stages of the AFFINITIE programme, compared to current practice. The interventions will be embedded within two rounds of the UK National Comparative Audit of Blood Transfusion (NCABT) focusing on patient blood management in surgery and use of blood transfusions in patients with haematological malignancies. The unit of randomisation will be National Health Service (NHS) trust or health board. Clusters providing care relevant to the audit topics will be randomised following each baseline audit (separately for each trial), with stratification for size (volume of blood transfusions) and region (Regional Transfusion Committee). The primary outcome for each topic will be the proportion of patients receiving a transfusion coded as unnecessary. For each audit topic a linked, mixed-method fidelity assessment and cost-effectiveness analysis will be conducted in parallel to the trial. DISCUSSION: AFFINITIE involves a series of studies to explore how A&F may be refined to change practice including two cluster randomised trials linked to national audits of transfusion practice. The methodology represents a step-wise increment in study design to more fully evaluate the effects of two enhanced feedback interventions on patient- and trust-level clinical, cost, safety and process outcomes. TRIAL REGISTRATION: http://www.isrctn.com/ISRCTN15490813.
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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 | 0.099 | 0.114 |
| Meta-epidemiology (narrow) | 0.012 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.084 | 0.019 |
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".