La Canadian CT Head Rule modifiée chez les patients victimes de traumatisme crânien léger sous antiagrégant plaquettaire
Bibliographic record
Abstract
Mild Traumatic brain injury is a frequent cause of admission in Emergency Departments, but with limited life-threatening consequences. Clinical decision rules such at the Canadian CT Head Rule have been proposed to help clinicians to decide for which patient they should perform a computed tomography scanner of the head. These rules exclude patient under anti platelet therapy cause they are at higher risk of intra cranial hemorrhage (ICH) than the general population, despite low complications. We conducted a retrospective, multicenter study to determine the performance of the modified Canadian CT Head Rule (M-CCTHR), a revised version of the Canadian CT Head Rule where being under APT is no longer a cause of exclusion. We included 1241 patients admitted for mild TBI under APT, calculated their M-CCTHR, and determined whether they met a composite outcome of neurosurgery, or hospitalization longer than 72 hours, or death related to ICH. In this study, we confirm that life threatening consequences are rare events. Importantly, we found that the M-CCTHR has a 48% sensitivity and a 97% negative predictive value for the primary outcome. Altogether, our study suggest that M-CCTHR could be a promising decision rule to identify patient under APT with mild TBI who are at low risk of life-threatening consequences.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".