How prior beliefs shape the interpretation of a randomized trial: a Bayesian re-examination of the CROSSFIRE trial
In brief
Prior beliefs put meaningful benefit from wrist fixation at 0% to 47%
Re-reading the CROSSFIRE trial with five evidence-based prior beliefs produced estimates ranging from 0% to 47% for a clinically meaningful benefit at 3 months. By 12 months, all approaches pointed to no meaningful difference. The analysis shows why readers can reach different conclusions from the same trial, and why making assumptions explicit matters when discussing evidence with patients.
- Journal
- The Journal of hand surgery, European volume (Q1)
- Published
- 21 October 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Rob Parisien, David Ring, Teun Teunis
- PMID
- 42908137
- DOI
- 10.1177/17531934261481232
Why clinicians should know about it
- Picked for Orthopedics and Sports Medicine (top studies of the week, 1 November 2026): Reexamines randomized evidence for distal radius fracture fixation
Abstract
A randomized trial reports the probability of the data given a hypothesis; the reader needs the probability of benefit given the data, and that quantity cannot be computed without a prior. We re-read the CROSSFIRE trial of distal radial fracture fixation in older adults under five literature-anchored prior beliefs, ranging from strong equivalence to anatomic restoration, against two thresholds for meaningful benefit. At 3 months, defensible readers' posterior probability of a clinically meaningful benefit spanned 0% to 47%; at 12 months, every prior converged on no meaningful difference. Every reader who moves from a trial report to a judgement about benefit brings a prior to that reading, whether or not it is stated. Disagreement among careful readers of the same trial can reflect different defensible priors rather than error, and making the prior explicit turns that disagreement into something colleagues and patients can inspect.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.