How reliable are reliability studies of fracture classifications?A systematic review of their methodologies
Bibliographic record
Abstract
Two independent reviewers performed a search in MEDLINE and EMBASE for fracture classification reliability studies. Data were obtained on classifications, image modalities, fracture selection processes, sample sizes and their justification, type and number of raters, practical issues for the classification sessions, statistical methods, and results. A 10-item checklist was devised for quality assessment of methodologies. 44 studies assessing 32 fracture classification systems were included. We found a wide variation of methodologies. For instance, the median number of raters was 5 (2-36) and the median number of fractures was 50 (10-200). This selection was considered representative in 17/44 of the studies. The true distribution of classification categories was estimated in 9 studies. The kappa coefficient was mostly used (39/44) to quantify the raters' agreement. Methodological issues are discussed. Given limitations in the use and interpretation of kappa coefficients, investigators should consider alternative methods that focus upon the accuracy of the classification systems. The development and adoption of a systematic methodological approach to the development and validation of fracture classification systems is needed.
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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 | 0.200 | 0.568 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".