Bibliographic record
Abstract
Background: Minor head trauma is one of the leading cause of emergency department visits worldwide. The Canadian Head CT-scan rule (CCHR) in minor head injury is an evidence-based aid in decision making as regards to use of CT-scans to detect head injury requiring neuro-intervention. It therefore avoids wastage of resources. The objective was to compare the number of CT-scans done for minor head injury as compared to the number that would have been done if the CCHR was applied.Methods: A retrospective study was done. All patients resenting with minor head injury (GCS 13-15) were identified from the hospital registry and their files obtained. Patients not meeting the CCHR criteria excluded. Ten parameters were extracted and tabulated.Results: Forty-one patients were included with three exclusions. 89% (n=34) of the patients presented with a 2-hour GCS of 13 or more. 11% (n=4) were suspicious of base skull fractures. 23% (n=9) had signs of open fracture. Vomiting was seen in 2 patients (5%). The mean age of patients was 29 years. 2 patients (5%) reported amnesia. All the patients had a CT scan done. Fourteen patients would have required CT scans had the rule been used. Positive findings were noted in seven of the patients who qualified and in three who did not. This demonstrated a 50% positive predictive value, a negative predictive value of 89%, a sensitivity 70% and 75% specificity.Conclusion: Use of CCHR would reduce unnecessary use of CT scans in minor head injury in this setup.Keywords: Canadian, head, CT scan, rule, minor head injury
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".