EOS Low-Dose Radiography
Bibliographic record
Abstract
BACKGROUND: Children with lower-limb-length discrepancy require repeated radiographic assessment for monitoring and as a guide for management. The need for accurate assessment of length and alignment is balanced by the need to minimize radiation exposure. We compared the accuracy, reliability, and radiation dose of EOS, a novel low-dose upright biplanar radiographic imaging system, at two different settings, with that of conventional radiographs (teleoroentgenograms) and computed tomography (CT) scanograms, for the assessment of limb length. METHODS: A phantom limb in a standardized position was assessed ten times with each of four different imaging modalities (conventional radiographs, CT scanograms, EOS-Slow, EOS-Fast). A radiation dosimeter was placed on the phantom limb, on a portion closest to the radiation source for each modality, in order to measure skin-entrance radiation dose. Standardized measurements of bone lengths were made on each image by consultant orthopaedic surgeons and residents and then were assessed for accuracy and reliability. RESULTS: The mean absolute difference from the true length of the femur was significantly lower (most accurate) for the EOS-Slow (2.6 mm; 0.5%) and EOS-Fast (3.6 mm; 0.8%) protocols as compared with CT scanograms (6.3 mm; 1.3%) (p < 0.0001), and conventional radiographs (42.2 mm; 8.8%) (p < 0.0001). There was no significant difference in accuracy between the EOS-Slow and EOS-Fast protocols (p = 0.48). The mean radiation dose was significantly lower for the EOS-Fast protocol (0.68 mrad; 95% confidence interval [CI], 0.60 to 0.75 mrad) compared with the EOS-Slow protocol (13.52 mrad; 95% CI, 13.45 to 13.60 mrad) (p < 0.0001), CT scanograms (3.74 mrad; 95% CI, 3.67 to 3.82 mrad) (p < 0.0001), and conventional radiographs (29.01 mrad; 95% CI, 28.94 to 29.09 mrad) (p < 0.0001). Intraclass correlation coefficients showed excellent (>0.90) agreement for conventional radiographs, the EOS-Slow protocol, and the EOS-Fast protocol. CONCLUSIONS: Upright EOS protocols that utilize a faster speed and lower current are more accurate than CT scanograms and conventional radiographs for the assessment of length and also are associated with a significantly lower radiation exposure. In addition, the ability of this technology to obtain images while subjects are standing upright makes this the ideal modality with which to assess limb alignment in the weight-bearing position. This method has the potential to become the new standard for repeated assessment of lower-limb lengths and alignment in growing children. CLINICAL RELEVANCE: This study assesses the reliability and accuracy of a diagnostic test used for clinical decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".