A 2D threshold of the condylar–C1 interval to maximize identification of patients at high risk for atlantooccipital dislocation using computed tomography
Bibliographic record
Abstract
OBJECTIVE Measurement of the occipital condyle-C1 interval (CCI) is important in the evaluation of atlantooccipital dislocation (AOD) in pediatric trauma patients. The authors studied a large cohort of children with and without AOD to identify a 2D measurement threshold that maximizes the diagnostic yield of the CCI on cervical spine CT scans obtained in trauma patients. METHODS This retrospective, single-center study included all children who underwent CT of the cervical spine at Primary Children's Hospital from January 1, 2011, through December 31, 2014, for trauma evaluation. Bilateral CCI measurements in the coronal (3 measurements per side) and sagittal (4 measurements per side) planes were recorded. Using an iterative method, the authors determined optimal cutoffs for the maximal CCI in each plane in relation to AOD. The primary outcome was AOD requiring occipitocervical fusion. RESULTS A total of 597 pediatric patients underwent cervical spine CT for trauma evaluation: 578 patients without AOD and 19 patients with AOD requiring occipitocervical fusion. The authors found a statistically significant correlation between CCI and age (p < 0.001), with younger patients having higher CCIs. Using a 2D threshold requiring a sagittal CCI ≥ 2.5 mm and a coronal CCI ≥ 3.5 mm predicted AOD with a sensitivity of 95%, a specificity of 73%, positive predictive value of 10.3%, and negative predictive value of 99%. The accuracy of this 2D threshold was 84%. CONCLUSIONS In the present study population, age-dependent differences in the CCI were found on CT scans of the cervical spine in a large cohort of patients with and without AOD. A 2D CCI threshold as a screening method maximizes identification of patients at high risk for AOD while minimizing unnecessary imaging studies in children being evaluated for trauma.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".