Low back pain-driven inpatient stays in the United States: a nationwide repeated cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP)-driven inpatient stays are resource-intensive and costly, yet data on contemporary national trends are limited. MATERIALS AND METHODS: This study used repeated cross-sectional analyses through a nationally representative sample (US National Inpatient Sample, 2016-2019). Outcomes included the rate of LBP-driven inpatient stays; the resource utilization (the proportion of receiving surgical treatments and hospital costs) and prognosis (hospital length of stay and the proportion of nonroutine discharge) among LBP-driven inpatient stays. LBP was classified as overall, nonspecific, and specific (i.e. cancer, cauda equina syndrome, vertebral infection, vertebral compression fracture, axial spondyloarthritis, radicular pain, and spinal canal stenosis). Analyses were further stratified by age, sex, and race/ethnicity. RESULTS: 292 987 LBP-driven inpatient stays (weighted number: 1 464 690) were included, with 269 080 (91.8%) of these for specific LBP and 23 907 (8.2%) for nonspecific LBP. The rate of LBP-driven inpatient stays varied a lot across demographic groups and LBP subtypes (e.g. for overall LBP, highest for non-Hispanic White 180.4 vs. lowest for non-Hispanic Asian/Pacific Islander 42.0 per 100 000 population). Between 2016 and 2019, the rate of nonspecific LBP-driven inpatient stays significantly decreased (relative change: 46.9%); however, substantial variations were found within subcategories of specific LBP-significant increases were found for vertebral infection (relative change: 17.2%), vertebral compression fracture (relative change: 13.4%), and spinal canal stenosis (relative change: 19.9%), while a significant decrease was found for radicular pain (relative change: 12.6%). The proportion of receiving surgical treatments also varied a lot (e.g. for overall LBP, highest for non-Hispanic White 74.4% vs. lowest for non-Hispanic Asian/Pacific Islander 62.8%), and significantly decreased between 2016 and 2019 (e.g. for nonspecific LBP, relative change: 28.6%). Variations were also observed for other outcomes. CONCLUSIONS: In the US, the burden of LBP-driven inpatient stays (i.e. rates of LBP-driven inpatient stays, resource utilization, and prognosis among LBP-driven inpatient stays) is enormous. More research is needed to understand why the burden varies considerably according to the LBP subtype (i.e. nonspecific and specific LBP as well as subcategories of specific LBP) and the subpopulation concerned (i.e. stratified by age, sex, and race/ethnicity).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".