Radiographs in screening for sacroiliitis in children: what is the value?
Bibliographic record
Abstract
BACKGROUND: We aimed to evaluate the diagnostic utility of pelvic radiographs versus magnetic resonance imaging (MRI) of the sacroiliac joints in children with suspected sacroiliitis. METHODS: This was a retrospective cross-sectional study of children with suspected or confirmed spondyloarthritis who underwent pelvic radiograph and MRI within 6 months of one another. Images were scored independently by five raters. Interrater reliability was calculated using Fleiss's kappa coefficient (κ). Test properties of radiographs for depiction of sacroiliitis were calculated using MRI global sacroiliitis impression as the reference standard. RESULTS: The interrater agreement for global impression was κ = 0.34 (95% CI 0.19-0.52) for radiographs and κ = 0.72 (95% CI 0.52-0.86) for MRI. Across raters, the sensitivity of radiographs ranged from 25 to 77.8% and specificity ranged from 60.8 to 92.2%. Positive and negative predictive values ranged from 25.9 to 52% and from 82.7 to 93.9%, respectively. The misclassification rate ranged from 6 to 17% for negative radiographs/positive MRI scans and from 48 to 74% for positive radiographs/negative MRI scans. When the reference standard was changed to structural lesions consistent with sacroiliitis on MRI, the misclassification rate was higher for negative radiographs/positive MRI scans (9-23%) and marginally improved for positive radiographs/negative MRI scans (33-52%). CONCLUSION: Interrater reliability of MRI was superior to radiographs for global sacroiliitis impression. Misclassification for both negative and positive radiographs was high across raters. Radiographs have limited utility in screening for sacroiliitis in children and result in a significant proportion of both false negative and positive findings versus MRI findings.
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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 | 0.030 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".