Is older age an appropriate criterion alone for ordering cervical spine computed tomography after trauma, by M Radmard et al. response to Ian Stiell's letter to the editor
Bibliographic record
Abstract
We are delighted that Dr. Stiell, one of the architects of the Canadian C-Spine Rule (CCR), has read and critiqued our recent article.1, 2 Dr. Stiell points out these important points: (1) that the CCR was intended to be applied to alert, stable patients with neck pain and (2) that missing 1.6% of fractures in the 65–75 (sic) year old range is too high a value to ignore. We agree with his comments. Our concern is that in practice, many clinicians scan trauma patients 65 years of age and older, even if they have no pain, and they do so, citing the CCR. This may be because many websites referring to the CCR do not specify neck pain as part of the inclusion criterion (see references on Physiopedia, Canadiem, Osteopathic, Researchgate, and Medbridge websites). Videos on YouTube under "CCR" also do not specify neck pain as an inclusion criterion.3, 4 Even Figure 1 of the NEJM article by Stiell et al. does not specify neck pain in the criteria for the CCR.5 We believe that continued work such as ours helps to support and clarify the original intent of the CCR. We reported that in a retrospective study, the asymptomatic fracture rate for patients 65–70 (not 65–75 in Dr. Stiell's letter) years old was four in 2192 (0.18%) and for all ages > 65 was 28/9455 (0.3%).1 These rates (0.18% and 0.3%) may be acceptable to omit imaging in asymptomatic patients in one's practice given that only 0.04% will be unstable fractures. The 1.6% rate of positive studies in the 65- to 70-year-old group (which for some may indeed be unacceptable despite the preponderance of stable fractures) has led us to explore additional factors to determine the subsets of these patients who may be able to forego cervical spine CT imaging. We recommend that additional study is needed potentially with the input of bioethicists and economists to identify an appropriate "cut point" to which we may align in achieving, as Dr. Stiell stated, "safe and rational use of cervical spine imaging." In the meantime, revisiting the scanning of asymptomatic patients over 65 years of age seems warranted. A well-constructed prospective study could provide more granularity in the data and allow for further refinement of a new 2024-2025 well-established decision support tool. The authors declare no conflicts of interest.
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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 | 0.006 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".