Clinical Magnification Error in Lateral Spinal Digital Radiographs
Bibliographic record
Abstract
STUDY DESIGN: Observational. OBJECTIVE: The objectives of this study were to determine the range of clinical magnification error in lateral spinal digital radiographs, and to determine the effect of body mass index (BMI) on this error. SUMMARY OF BACKGROUND DATA: The magnification error in plain radiographs is often estimated at 15% to 30%. The variability of this error in digital spinal radiographs has not been assessed. METHODS: An analysis of 250 patients with digital radiographs and computed tomography (CT)/magnetic resonance images (MRIs) was performed. Digital imaging software was used to measure the anteroposterior vertebral body dimensions at C2, C5, L1, and L4. Magnification values were determined in comparison to CT/MRI. CT measurements were also compared with MRI. BMI for each patient was obtained by chart review. RESULTS: The mean magnification at the cervical spine (C2 and C5 combined) was 1.22 +/- 0.01, with a range of 1.06 to 1.57 (n = 198, STDEV = 0.08); at the lumbar spine (L1 and L4 combined) it was 1.31 +/- 0.01, with a range of 1.09 to 1.63 (n = 300, STDEV = 0.08). The difference between the mean anteroposterior vertebral body dimensions as measured on CT and MRI was < 0.1 mm (n = 135, P < 0.2514, paired t test). There was a significant positive correlation between BMI and magnification at both the cervical and lumbar spines by linear regression (Cervical: n = 99; P = 0.0019; Lumbar: n = 150; P < 0.0001). There was a significant difference in magnification between nonobese and obese patients at both the cervical and lumbar levels. Cervical: 1.19 +/- 0.01 magnification for nonobese (n = 144), versus 1.26 +/- 0.01 for obese (n = 39) (P < 0.0001). Lumbar: 1.28 +/- 0.01 (n = 208), versus 1.38 +/- 0.01 (n = 78) (P < 0.0001), respectively. CONCLUSION: Linear clinical measurements obtained on digital radiographs are subject to significant magnification errors at both the cervical and lumbar spines. This error correlates to the patient's BMI. Consequently, clinical decision-making that is based on linear measurements obtained from radiographs that do not account for this error is invalid. In a scenario where this measurement is crucial (e.g., dynamic radiographs), this error can be corrected by comparison to morphometric data from a CT/MRI.
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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".