51 CT Head and Cervical Spine Audit in Patients Over the Age of 65: A District General Hospital Perspective
Bibliographic record
Abstract
Abstract Introduction Cervical spine (c-spine) injury has a high morbidity and mortality in patients over the age of 65; more than 60% result from falls from standing height (Beedham et al., 2019). The Canadian Cervical Spine Rule (Stiell et al., 2001) deems that there is a high risk of c-spine fracture if any of the following apply: The c-spine cannot be cleared clinically if the patient fits any of the above criteria. Imaging should be considered. As a result of recent clinical experiences Trust Guidelines at Stoke Mandeville Hospital now reflect this evidence (Hadley et al., 2019). Methods Fifty patients over the age of 65 who had a computerised tomography (CT) head scan in the Emergency Department (ED) following a traumatic head injury were randomly selected over a 1 month period. Cases were checked for examination of c-spine and/or CT c-spine. Results of the first cycle of the audit were presented at an ED Education Meeting. Indications for CT c-spine were displayed in poster format around the ED. Following these interventions, a re-audit was carried out using the same methodology. Results In fifty patients aged over 65 attending ED during one month, 16% had a CT c-spine in addition to a CT head. There was documented c-spine examination of 16% of those without CT c-spine on admission. In the re-audit 38% of the fifty patients who had a CT head underwent CT c-spine. In the group that did not have imaging of the c-spine, the proportion with documented cervical spine examination on admission remained the same (16%). Conclusion There was a 137.5% increase in the number of patients aged over 65 who appropriately underwent a CT c-spine as per Trust and National guidelines. Simple interventions (staff education and posters within the ED) were sufficient to significantly alter practice. Current trauma triage is not optimal for older patients who are reviewed by more junior doctors, less likely to be transferred to Major Trauma Centres and more likely to die than younger patients with similar injuries (Major Trauma In Older People 2017 Report). An older person’s trauma team in ED with age-appropriate triage would lead to appropriate imaging in a timely fashion, potentially improving the morbidity and mortality of these vulnerable patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".