The Effect of Two Factors on Interobserver Reliability for Proximal Humeral Fractures
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to assess whether training observers and simplifying proximal humeral fracture classifications improve interobserver reliability among a large number of orthopaedic surgeons. METHODS: One hundred eighty-five observers were randomized to receive training or no training in a simple classification for proximal humeral fractures before evaluating preoperative radiographs of a consecutive series of 30 patients who were treated with open reduction and internal fixation. RESULTS: The overall interobserver reliability of the simple proximal humeral fracture classification system was low and not significantly different between the training and the no training group (κ = 0.20 and κ = 0.18, respectively; P = 0.10). Subgroup analyses showed that training improved the agreement among surgeons who have been in independent practice ≤5 years (κ = 0.23 versus κ = 0.14; P < 0.001), surgeons from the United States (κ = 0.23 versus κ = 0.16; P = 0.002), and general orthopaedic surgeons (κ = 0.42 versus κ = 0.15; P = 0.021). DISCUSSION: Simplifying classifications and training observers did not improve the interobserver reliability for the diagnosis of proximal humeral fractures. However, training observers improved interobserver reliability of a simple proximal humeral fracture classification system among surgeons from the United States and, in particular, younger and less specialized surgeons. This finding may suggest that our interpretations of radiographic information might become more fixed and immutable with experience.
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 | 0.049 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".