Evaluation of the impact of the Canadian CT head rule on British practice.
Bibliographic record
Abstract
BACKGROUND: The Canadian CT head rule has been developed to identify which adults with minor head injuries require computed tomography (CT). This is hoped will reduce the number of CT scans performed for minor head injury in North America. It was unclear whether applying the rule would reduce or even increase the number of CT scans requested in UK emergency departments. METHODS: A retrospective evaluation was conducted of all adults who presented after minor head injuries to Addenbrooke's emergency department. Clinical information about patients with head injuries is collected on standardised forms. A dataset was constructed to predict how many patients would require head CT scans if the Canadian CT rule was applied. RESULTS: 1489 adults presented after minor head injury over a seven month period. Seventy four of these had CT scans for head injury, applying the Canadian CT head rule would have resulted in 132 CT scans being requested. This is significantly more (p>0.001). This would have resulted in a 68% increase in costs. INTERPRETATION: The Canadian CT head rule would result in an increase in the number of CT scans requested for minor head injuries. This increased cost must be considered against the 488 skull radiographs that were requested during the study period.
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 | 0.019 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".