Attention to Detail: The Effect of Fluoroscopic Parallax on Limb Alignment Assessment During Corrective Osteotomy
Bibliographic record
Abstract
BACKGROUND: The accuracy of a corrective osteotomy is dependent on many factors. One error rarely considered is using noncentered fluoroscopic imaging to assess intraoperative alignment. This study quantified the coronal alignment error produced by visual parallax per interval changes in vertical and horizontal positioning of the C-arm and alignment rod during intraoperative evaluation. METHODS: Unilateral hip, ankle, and knee fluoroscopic images were obtained from a single intact cadaveric specimen. A center-center fluoroscopic image was obtained by moving the C-arm appeared in the center square of the nine-box grid. With the base of the C-arm stationary, the radiograph generator/intensifier portion of the C-arm was translated medially until the target bone appeared on the edge of the fluoroscopic image. RESULTS: One hundred eight images were obtained. Measurement error increased by an average of 14% per 10 mm of horizontal C-arm offset. Minimal effect was seen if the obtained image was within 5 mm of the true center; however, once 55 mm of offset was reached, all experimental conditions resulted in at least 10 mm of parallax error. CONCLUSION: Our results demonstrate that small variations in C-arm positioning can create statistically significant inaccuracies when assessing limb alignment using intraoperative fluoroscopy.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".