A Structured Review Addressing the Use of Radiographic Measures of Alignment and the Definition of Acceptability in Patients with Distal Radius Fractures
Bibliographic record
Abstract
BACKGROUND: Standard radiographs are routinely used in clinical care to characterize the severity of a distal radius fracture and to monitor patients following a distal radius fracture. The objective of this review was to describe the range and variability of radiographic measures described in the literature in patients following a distal radius fracture. METHODS: A structured literature review was conducted using the Embase and PubMed databases. Inclusion criteria included full-text publications which employed radiographic measures to examine 100 or more participants following a distal radius fracture. A standardized data extraction form was used to identify study design, fracture classification systems, the types of and definitions of radiographic measurements, and acceptability criteria following distal radius fractures. RESULTS: From an initial 263 studies, 31 studies were included in the final data extraction process. A narrative synthesis of the articles included in this review indicated that there was a set of commonly used radiographic measurements examined in patients with a distal radius fracture which included radial inclination, volar/dorsal tilt, intra-articular step/gap, and a measure of ulnar variance/radial shortening. While 52 % of studies referenced or published a standardized measurement technique, there was substantial variability in the actual description of each radiographic measurement performed. CONCLUSIONS: Substantial variability in how radiographic measurements are defined in large clinical studies as seen in this review suggest a need for consensus on the assessment and interpretations of radiographic measures used in patients following a distal radius fracture. Guidelines for radiographic measures should be established to ensure consistency between research and treatment centers.
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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 | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".