Classification systems for distal radius fractures
Bibliographic record
Abstract
Background and purpose - The reliability of conventional radiography when classifying distal radius fractures (DRF) is fair to moderate. We investigated whether reliability increases when additional computed tomography scans (CT) are used. Patients and methods - In this prospective study, we performed pre- and postreduction posterior-anterior and lateral radiographs of 51 patients presenting with a displaced DRF. The case was included when there was a (questionable) indication for surgical treatment and an additional CT was conducted within 5 days. 4 observers assessed the cases using the Frykman, Fernández, Universal, and AO classification systems. The first 2 assessments were performed using conventional radiography alone; the following 2 assessments were performed with an additional CT. We used the intraclass correlation coefficient (ICC) to evaluate reliability. The CT was used as a reference standard to determine the accuracy. Results - The intraobserver ICC for conventional radiography alone versus radiography and an additional CT was: Frykman 0.57 vs. 0.51; Fernández 0.53 vs. 0.66; Universal 0.57 vs. 0.64; AO 0.59 vs. 0.71. The interobserver ICC was: Frykman: 0.45 vs. 0.28; Fernández: 0.38 vs. 0.44; Universal: 0.32 vs. 0.43; AO: 0.46 vs. 0.40. Interpretation - The intraobserver reliability of the classification systems was fair but improved when an additional CT was used, except for the Frykman classification. The interobserver reliability ranged from poor to fair and did not improve when using an additional CT. Additional CT scanning has implications for the accuracy of scoring the fracture types, especially for simple fracture types.
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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".