Does Every Patient Require Imaging after Cervical Spine Trauma? A Knowledge Translation Project to Support Evidence-Informed Practice for Physiotherapists
Bibliographic record
Abstract
Purpose: This article evaluates, describes, and addresses a gap in British Columbia physiotherapists' knowledge of the decision making required for the diagnostic imaging of patients after traumatic neck injury. Method: An online survey of orthopaedic physiotherapists in British Columbia was undertaken to explore their awareness of, knowledge of, and attitudes toward the Canadian Cervical Spine Rule (C-Spine Rule) and decision making regarding the need for diagnostic imaging in managing patients with traumatic neck injury. The survey included questions about managing clinical scenarios; respondents' awareness, knowledge, and use of a specific clinical decision rule—the C-Spine Rule—and any perceived barriers to using clinical practice guidelines in general and the C-Spine Rule in specific. The survey also included questions about the facilitators of and barriers to using the C-Spine Rule. These data were used to guide development of a tool kit to facilitate use of the rule. Results: Of 889 physiotherapists, 467 (52.5%) completed the survey. Given a scenario in which imaging was indicated according to the C-Spine Rule, 95.2% of the respondents correctly recommended imaging. However, in a scenario in which imaging was not indicated, 42.7% incorrectly recommended it. The barriers to using the guidelines included their perceived rigidity, role limitation, and reliance on clinical judgment. The results indicated a need for, and guided development of, resources to facilitate the use of the C-Spine Rule by British Columbia physiotherapists. Conclusions: We identified a gap in the knowledge of British Columbia physiotherapists in identifying which patients were most likely to require imaging after sustaining a traumatic neck injury. We developed a tool kit to address these barriers. British Columbia physiotherapists have accessed this resource extensively. Evaluating its impact on clinical practice, although desirable, was not feasible.
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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 | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".