Shifting Trends in the Epidemiology of Cervical Spine Injuries: An Analysis of 11,822 Patients from the National Electronic Injury Surveillance System over Two Decades
Bibliographic record
Abstract
Cervical spine injuries (CSIs) are heterogeneous in nature and often lead to long-term disability and morbidity. However, there are few recent and comprehensive epidemiological studies on CSI. The objective of this study was to characterize recent trends in CSI patient demographics, incidence, etiology, and injury level. The National Electronic Injury Surveillance System was used to extract data on CSIs from 2002 to 2022. Weighted national estimates of CSI incidence were computed using yearly population estimates interpolated from U.S. census data. Data analysis involved extracting additional information from patient narratives to categorize injury etiology (i.e., fall) and identify CSI level. K-means clustering was performed on cervical levels to define upper versus lower cervical injuries. Appropriate summary statistics including mean with 95% confidence intervals and frequency were reported for age, sex, race, ethnicity, etiology, and disposition. Age between groups was compared using an independent weighted Z -test. All categorical variables were compared using Pearson chi-squared tests with Bonferroni correction for multiple comparisons. Ordinary least squares linear regression was used to quantify the rate of change of various metrics with time. A total of 11,822 patient records met the study criteria. The mean age of patients was 62.4 ± 22.7 years, 52.4% of whom were male and 61.4% of whom were White, 7.4% were Black, 27.8% were not specified, and the remaining comprised a variety of ethnicities. The most common mechanism of CSI was a fall (67.3%). There was a significant increase in the incidence of cervical injuries between 2003 and 2022 ( p < 0.001). Unbiased K-means clustering defined upper cervical injuries as C1–C3 and lower cervical injuries as C4–C7. The mean age of patients with upper CSIs was 72.3 ± 19.6, significantly greater than the age of those with lower CSIs (57.1 ± 23.1, p < 0.001). Compared with lower CSI, White patients were more likely to have an upper CSI (67.4% vs. 73.7%; p < 0.001). While Black/African American (7.5% vs. 3.8%) and Hispanic (2.5% vs. 1.0%) patients were more likely to have a lower CSI ( p < 0.001). Our study identified a significant increase in the incidence of CSIs over time, which was associated with increasing patient age. Our study detected a pragmatic demarcation of classifying upper injuries as C1–C3 and lower cervical injuries as C4–C7. Upper injuries were seen more often in older, White females who were treated and admitted, and lower injuries were seen more often in young, Black male patients who were released without admission.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".