Factors Associated with Overutilization of Computed Tomography of the Cervical Spine
Bibliographic record
Abstract
Introduction: Despite the wide availability of clinical decision rules for imaging of the cervical spine after a traumatic injury (eg, NEXUS C-spine rule and Canadian C-spine rule), there is significant overutilization of computed tomography (CT) imaging in patients who are deemed to be at low risk for a clinically significant cervical spine injury by these clinical decision rules. The purpose of this study was to identify the major factors associated with the overuse of CT cervical spine imaging using a logistic regression model.Methods: This was a retrospective review of all adult patients who underwent CT cervical spine imaging for evaluation of a traumatic injury at a tertiary academic emergency department (ED) and three affiliate community EDs in January and February 2019. We performed multivariable logistic regression to identify factors associated with obtaining CT cervical spine imaging despite low-risk classification by the NEXUS C-spine Rule.Results: A total of 1,051 patients underwent CT cervical spine imaging for traumatic indications during the study period, and 889 patients were included in the analysis. Of these patients, 376 (42.3%) were negative by the NEXUS C-spine rule. Variables that were associated with increased likelihood of unnecessary imaging included age over 65, Emergency Severity Index (ESI) score 2 and 3, arrival as a walk-in, and anticoagulation status. Patients who presented to the tertiary academic ED had a significantly lower likelihood of unnecessary imaging. Twenty-one patients (2.4%) were found to have cervical spine fractures on imaging, two of whom were negative by the NEXUS C-spine rule, but neither had a clinically significant fracture.Conclusion: Cervical spine imaging is vastly overused in patients presenting to the ED with traumatic injuries, as adjudicated using the NEXUS C-spine rule as a reference standard. Older age, ESI level, arrival as a walk-in, and taking anticoagulation drugs were associated with overutilization of CT imaging. Conversely, presenting to the tertiary academic ED was associated with a lower likelihood of undergoing unnecessary imaging. This model can guide future interventions to optimize ED CT utilization and minimize unnecessary testing.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".