Interobserver Reliability of Classification Systems to Rate the Quality of Femoral Neck Fracture Reduction
Bibliographic record
Abstract
OBJECTIVES: We compared the interrater reliability of five classification systems and surgeons' global impressions of the quality of femoral neck fracture reduction. METHODS: Six orthopedic trauma surgeons, six orthopedic nontrauma surgeons, and six orthopedic residents from three sites each rated 50 radiographs of postoperative femoral neck fractures fixated with cannulated screws or a sliding hip screw, using their overall impression, the Garden Index, Lowell's criteria, Lindquist and Tornkvist's criteria, the Western Infirmary Glasgow (WIG) angle, and standards established by a working group of orthopedic trauma surgeons. RESULTS: Reliability estimates for the Garden Index, Lowell's criteria, and the working group standards all fell within the range of moderate agreement [intraclass correlation coefficient (ICC) range 0.41-0.48], with no instrument achieving higher reliability than reviewers' overall impressions (ICC 0.49, 95% CI 0.39-0.61). Reviewers reached only fair agreement using Lindquist and Tornkvist's criteria and the WIG angle (kappa = 0.27 and 0.39, respectively). Trauma surgeons consistently achieved higher agreement than did nontrauma surgeons and trainees. CONCLUSIONS: Future studies using quality of reduction as an outcome measure or exploring the prognostic importance of reduction quality should measure this with trauma surgeons' overall impression, rather than with less experienced assessors or using one of the alternative instruments.
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 | 0.053 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".