Projected paediatric cervical spine imaging rates with application of NEXUS, Canadian C-Spine and PECARN clinical decision rules in a prospective Australian cohort
Bibliographic record
Abstract
Background Clinical decision rules (CDRs) are commonly used to guide imaging decisions in cervical spine injury (CSI) assessment despite limited evidence for their use in paediatric populations. We set out to determine CSI incidence, imaging rates and the frequency of previously identified CSI risk factors, and thus assess the projected impact on imaging rates if CDRs were strictly applied as a rule in our population. Methods A single-centre prospective observational study on all aged under 16 years presenting for assessment of possible CSI to a tertiary paediatric emergency department over a year, commencing September 2015. CDR variables from the National Emergency X-Radiography Utilization Study (NEXUS) rule, Canadian C-Spine rule (CCR) and proposed Paediatric Emergency Care Applied Research Network (PECARN) rule were collected prospectively and applied post hoc. Results 1010 children were enrolled; 973 had not received prior imaging. Of these, 40.7% received cervical spine imaging; 32.4% X-rays, 13.4% CT scan and 3% MRI. All three CDRs identified the five children (0.5%) with CSI who had not received prior imaging. If CDRs were strictly applied as a rule for imaging, projected imaging rates in our setting would be as follows: NEXUS-44% (95% CI 41% to 47.4%), CCR-at least 48.4% (95% CI 45.3% to 51.7%) and PECARN-68% (95% CI 65.1% to 71.1%). Conclusion CSIs were rare (0.5% of our cohort), however, 40% of children received imaging. CDRs have been designed to guide imaging decisions; if strictly applied as a rule for imaging, the CDRs assessed in this study would increase imaging rates. Projected rates differ considerably depending on the CDR applied. These findings highlight the need for a validated paediatric-specific cervical spine imaging CDR.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".