Measurement of vertebral rotation using a three-dimensional ultrasound image
Bibliographic record
Abstract
Materials and methods Three cadaveric vertebrae T7, L1, and L3 were scanned with a 3D medical ultrasound system. The rotation angles of the cadaveric vertebra were recorded during the experiment. Nine sets of ultrasound data, from 0 to 40° with 5° increments, were recorded from each vertebra (27 sets in total). An in-house program was used to reconstruct the 3D vertebra images. The rotation of each reconstructed vertebra was determined by the angle between the line going through either the centres of laminae (L-L) or the centres of transverse processes (TPTP) and a reference vertical plane. This reference plane was defned from the position sensor parallel to the surface of the transducer. Three raters, who were blinded with the rotation information, used the images to measure the rotation in 3 sessions. In each session, the raters used the mouse pointer to select L-L or TP-TP according to their knowledge of vertebral anatomy. The program received the 3D coordinates of these points and calculated the VR. Intra-class correlation coefficients (ICCs) (two-way random and absolute agreement) were used to calculate the intraand inter-reliability. The mean absolute difference (MAD±SD) and the range of difference (RD) between the true values and the average measurements of each rater were also computed to evaluate the accuracy of methods. Results When rotation was greater than 30° for both L1 and L3, all raters found it difficult to determine one of the lamina areas because it could not be displayed due to ultrasound blocking. Therefore, the corresponding measurements were excluded. The intra-reliability (L-L, TP-TP) for the three raters were (0.987, 0.991), (0.989, 0.998) and (0.997, 1.000), respectively; meanwhile, the inter-reliability were 0.991 for (L-L) and 0.992 for (TP-TP). All ICC values were greater than 0.98 indicating both methods were highly reliable. The MAD±SD values (L-L, TP-TP) for the three raters were (1.5±0.3°, 1.2±0.2°), (1.6±0.3°,1.3 ±0.3°), and (1.7±0.5°, 0.9±0.2°), respectively. The RD (L-L, TP-TP) were (0-4.5°, 0-3.5°), (0-5.1°, 0-4.3°), and (0-5.1°, 0-2.8°) for the three raters, respectively.
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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) | 0.000 | 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".