Bibliographic record
Abstract
Description: The Canadian C-Spine Rule was designed in 2001 to assist clinicians assess the need for imaging in people who present to the emergency department with a cervical spine injury following blunt trauma. Specifically, this clinical decision rule was developed for use in adults who are alert (score of 15 on the Glasgow Coma Scale), stable and in whom a clinically important cervical spine injury is a concern (eg, unstable fracture, dislocation).1Instructions and scoring: The Canadian C-Spine Rule is based on three high-risk criteria (age ≥ 65 years, dangerous injury mechanism, paresthesia in extremities), five low-risk criteria (simple rear-end motor vehicle collision, sitting position in the emergency department, ambulatory at any time, delayed onset of neck pain; absence of midline cervical-spine tenderness), and the ability of the person to rotate their neck.2Reliability, validity and sensitivity to change: The Canadian C-Spine Rule has good-to-excellent inter-rater reliability when applied by physicians (kappa = 0.63), nurses (kappa = 0.80) and paramedics (kappa = 0.93).2, 3 The sensitivity of the Canadian C-Spine Rule has been reported to range from 90 to 100%, whereas specificity has ranged from 1 to 77%.4 The large range in specificity reflects the heterogeneity between studies in the number of people who unnecessarily receive imaging (ie, people who do not have a serious cervical spine injury but are still referred for imaging). However, the rule itself errs on the side of caution, as clinicians will not miss a clinically important cervical spine injury. In the only direct comparison, the Canadian C-Spine Rule was found to have better diagnostic accuracy than the National Emergency X-Radiography Utilization Study (NEXUS) criteria,5 which form another widely used clinical decision rule.4
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.047 |
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".