Patterns of change in prehospital spinal motion restriction: A retrospective database review
Bibliographic record
Abstract
BACKGROUND: Acute management of trauma patients with potential spine injuries has evolved from uniform spinal immobilization (SI) to spinal motion restriction (SMR). Little research exists describing how these changes have been implemented. This study aims to describe and analyze the practice of SMR in one emergency medical services (EMS) agency over the time frame of SMR adoption. METHODS: This was a retrospective database review of electronic patient care reports from 2009 to 2020. The effects of key practice changes (revised documentation and a collar-only treatment option) were analyzed in an interrupted time series using the rate of SI/SMR as the primary outcome. Secondary outcomes included patient age, sex, acuity, mechanism of injury, treatment provided, cervical collar size, and positioning. These were assessed for changes from year to year by Poisson regression. Associations between patient and treatment characteristics were investigated with binomial logistic regression. RESULTS: There were 25,747 instances of SI/SMR included. Among all patients, the median age was 40 (interquartile range 24-56), 58% (14,970) were male, and 20% (5062) were high-acuity. The rate of SI/SMR declined from 31.2 to 12.7 treatments per 100 trauma calls per month. The proportion of high-acuity patients increased by 9.6% per year on average (95% CI 8.7%-10.0%). When first available, collar-only treatment was provided to 47% of patients, rising by 6.3% per year (95% CI 3.2%-9.5%) to 60% in 2020. Collar-only treatment (compared to board-and-collar) was more likely to be applied to low-acuity patients (as compared to high): odds ratio 3.01 (95% CI 2.64-3.43). CONCLUSIONS: This study shows decreasing SI/SMR treatment and changing patient and practice characteristics. These patterns of care cannot be attributed solely to formal protocol changes. Similar patterns and their possible explanations should be investigated elsewhere.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".