Predicting Clinically Significant Brain Injuries Following Mild TBI: A Comparative Study of Canadian CT Head Rule and New Orleans Criteria at a National Trauma Centre.
Bibliographic record
Abstract
Background: Mild traumatic brain injury (mTBI) is one of the most common injuries treated at any trauma centre. Whereas the general use of CT for all patients with mTBI is inefficient and wasteful, the omission of a clinically important brain injury is not desirable. Several guidelines have been developed to assist physicians in determining who actually needs a head CT. For this reason, the Canadian CT Head Rule (CCHR) and the New Orleans Criteria (NOC) were compared in this study on their efficacy in predicting surgically significant brain injuries and the need for neurosurgical intervention. Methodology: The research was a prospective cross-sectional study at a level 1 trauma centre that received ethical approval from the Hospital. Consenting adult patients who presented with mild TBI within 24 hours were recruited. They were assessed with the NOC and CCHR, whose decisions were compared with each other and with CT head findings. Results: A total of 103 patients were successfully enrolled, males were 91 and females were 12, with a mean age of 32.48±12.27 years old. The NOC guideline had a sensitivity (88.6%), specificity (21.4%), positive predictive value (47.0%) and negative predictive value (70.6%) of clinically significant brain injury; while CCHR guideline showed sensitivity (86.4%), specificity (30.4%), positive predictive value (49.4%) and negative predictive value (73.9%) of clinically significant brain injury (table 3), however, statistically were not significantly different with P-value of 0.39. Similarly, there was no statistically significant difference between the two guidelines for the need for neurosurgical intervention, as the P-value was 0.48. Conclusion: Following the findings, this study suggests that either NOC or CCHR is safe to be used for ordering a head CT for patients with mild TBI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".