Will a New Clinical Decision Rule Be Widely Used? the Case of the Canadian C‐Spine Rule
Bibliographic record
Abstract
OBJECTIVES: The reasons why some clinical decision rules (CDRs) become widely used and others do not are not well understood. The authors wanted to know the following: 1) To what extent is widespread use of a new, relatively complex CDR an attainable goal? 2) How do physician perceptions of the new CDR compare with those of a widely used rule? 3) To what extent do physician subgroups differ in likelihood to use a new rule? METHODS: A survey of 399 Canadian emergency physicians was conducted using Dillman's Tailored Design Method for postal surveys. The physicians were queried regarding the Canadian Cervical-Spine Rule (C-Spine Rule). Results were analyzed via frequency distributions, tests of association, and logistic regression. RESULTS: Response rate was 69.2% (262/376). Most respondents (83.6%) reported having already seen the Canadian C-Spine Rule, while 63.0% reported already using it. Of those who did not currently use the rule, 74.2% reported that they would consider using it in the future despite the fact that, compared with another widely used rule (the Ottawa Ankle Rules), the C-Spine Rule was rated as less easy to learn (z = 6.68, p < 0.001), remember (z = 7.37, p < 0.001), and use (z = 5.90, p < 0.001). Those who had never seen the rule before were older (chi2(2) = 5.10, p = 0.007) and more likely to work part-time (chi2(2) = 7.31, p = 0.026). The best predictors of whether the rule would be used was whether it had first been seen during training (odds ratio [OR], 2.62; 95% confidence interval [CI] = 1.14 to 6.04), was perceived as an efficient use of time (OR, 4.44; 95% CI = 1.12 to 16.89), and was too much trouble to apply (OR, 0.25; 95% CI = 0.08 to 0.77). CONCLUSIONS: Widespread use of a relatively complex rule is possible. Older and part-time physicians were less likely to have seen the Canadian C-Spine Rule but not less likely to use it once they had seen it. Targeting hard-to-reach subpopulations while stressing the safety and convenience of these rules is most likely to increase use of new CDRs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".