Canadian C-spine rule (CCR) versus national emergency X- radiography utilization study (NEXUS) for screening cervical spine injury
Bibliographic record
Abstract
Background: There is uncertainty about the optimal guidelines for screening clinically important cervical spine injuries following blunt trauma. The Canadian C Spine rule (CCR) and the National Emergency X Radiography Utilization system (NEXUS) are two widely accepted guidelines to help emergency physicians and trauma surgeons in case of cervical blunt trauma. Methods: We conducted a prospective analytical study between June 2019 to July 2020 at Mahatma Gandhi Mission’s Hospital (MGM), Navi Mumbai comparing the diagnostic accuracy of CCR and NEXUS as applied to alert patients with trauma who were in stable condition.Result: Among the 400 trauma patients randomly selected for the study 280(70%) patients were male and 120(30%) were female. According to the NEXUS guidelines, 202(50.5%) patients and according to the CCR guidelines 210(52.5%) patients required radiography, however as per the results obtained from the cervical spine imaging only 20 (5%) patients had cervical spine injury. Both the NEXUS and CCR guidelines had similar true positive and false negative rates of 5% and 0.5% respectively. Both tests had sensitivity of about 91% and specificity 56.54% and 45.54% respectively.Conclusion: Based on studies with modest methodologic quality and only one direct comparison indicated that both CCR and NEXUS carry the same sensitivity for evaluating which patients need to undergo cervical spine imaging. Further the NEXUS guidelines have the same effectiveness as CCR for determining which patients must be subjected to cervical spine imaging.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".