Canadian c-spine criteria and nexus in the spinal trauma: comparison at a tertiary referral hospital in Turkey
Bibliographic record
Abstract
Objective: Spinal trauma and the ensuing neurological problems transform a person’s social life and result in significant economic and non-economic burden. We compared the diagnostic performances of the National Emergency X-Radiography Utilization Study (NEXUS) Low-Risk Criteria (NLC) with the Canadian C-Spine Rule (CCSR) criteria in identifying lesions. Methods: This retrospective study was conducted on 724 patients after obtaining approval from the ethical board of the hospital. The demographic characteristics of the patients (age, gender), their medical histories, season, trauma occurrence mechanism, hospital arrival time following the development of spinal trauma, their Glasgow Coma Score at the time of admission, their complaints at the time of admission (such as pain, paresthesia, and loss of muscle strength), their spinal trauma lesion levels, and compatibility of the applied viewing methods with the NEXUS and CCSR criteria were collected from the patients’ files. Results: A total of 2,442 cases were diagnosed with spinal trauma. For patients with a spinal fracture, the sensitivity and specificity of CCSR were 99.7% and 17.9%, respectively, while the sensitivity and specificity of NEXUS were 97.6% and 27.2%, respectively. Positive predictive value (PPV) and negative predictive value (NPV) of CCSR were, respectively, 16.3% and 99.7%, while the PPV and NPV of NEXUS were 17.7% and 98.6%, respectively. Conclusions: This study showed that the CCSR criteria are more sensitive than the Nexus criteria.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".