Decreasing Rates of Patients With Low Back Pain Presenting to Emergency Departments
Bibliographic record
Abstract
OBJECTIVE: To analyze trends in low back pain presentations to US emergency departments (EDs) and identify associated risk factors and barriers to care. STUDY DESIGN: Retrospective analysis using data from the National Electronic Injury Surveillance System (NEISS) from 2014 to 2023. SUMMARY OF BACKGROUND DATA: Low back pain is one of the most common reasons for seeking medical care in the United States. Post-COVID-19, many older adults seem to defer care. Understanding trends in low back pain incidence can highlight potential improvements in prevention and gaps in health care access. METHODS: NEISS data from January 1, 2014, to December 31, 2023, were queried for lower trunk injuries coded as strain/sprain. Narratives consistent with low back pain were included. Demographic and injury-related data were analyzed to estimate trends and outcomes. RESULTS: The query identified 48,829 cases of low back pain, corresponding to a national estimate (NE) of 2,001,384 cases. Low back pain incidence decreased significantly over the study period [P<0.001, β=-0.967, 95% CI: (-25216.56, -16296.58)]. Most cases involved patients aged 46-65 (29.8%) and 31-45 (28.8%). Common causes of injury included stairs (8.8%) and flooring (7%), with 51.3% occurring at home. Males and females accounted for 50.8% and 49.2% of cases, respectively. Hospitalization rates averaged 1.2%, peaking at 2.4% in 2022. CONCLUSIONS: Low back pain incidence in US EDs has declined over the past decade, possibly reflecting better prevention or alternative care pathways. However, steady hospitalization rates suggest injury severity remains unchanged. Further research is needed to assess care-seeking patterns, risk factors, and prevention strategies to address the burden of low back pain. LEVEL OF EVIDENCE: Level III.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".