Factors which affect the application and implementation of a spinal motion restriction protocol by prehospital providers in low resource settings: a scoping review
Bibliographic record
Abstract
The South African Professional Board for Emergency Care prehospital Clinical Practice Guideline (CPG) recommends that emergency medical services (EMS) make use of the National Emergency X Radiography Utilization Study (NEXUS) rule and Canadian C-spine Rule (CCSR) when managing traumatic spinal injury. However, the safety and effectiveness of prehospital clinical spinal clearance or spinal motion restriction (SMR) decision support tools within poorly resourced settings are unclear. We conducted a scoping review on clinical spinal clearance and selective SMR decision support tools which aimed at identifying possible barriers to their implementation, safety, and effectiveness when used by EMS personnel. Studies were included if they described the use of clinical spinal clearance or SMR decision tools in first line management of blunt trauma patients by medical practitioners in the Emergency Department (ED) or by EMS personnel working in a prehospital setting. After screening, 42 documents fulfilled the inclusion criteria. Several selective SMR decision support tools have been implemented in the prehospital setting, the most common of which were those based on the NEXUS and the CCSR tools. Only one study evaluated the safety and efficacy of the NEXUS rule when used by EMS personnel. The limited prehospital literature available investigating either the NEXUS rule or CCSR therefore makes it difficult to determine its appropriateness for adoption and implementation by EMS personnel in other prehospital settings such as that of South Africa. Furthermore, commonly found prehospital NEXUS-based decision tools presented with unique challenges related to the subjective nature of some of the individual components of the decision tool. This leaves the decision tool open to interpretation by examiners and is especially relevant in settings, such as South Africa, where there are many different levels in scope of practices. This increases the risk of the patient being either under-triaged or over-triaged. More studies are therefore needed to definitively assess for the safety, efficacy and effectiveness of clinical spine clearance within the prehospital setting. It is believed that a selective SMR decision tool which has more specific instructions for the prehospital practitioner may be able to accommodate such challenges and is an area which needs further investigation.
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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 | 0.046 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".