Appropriateness of Computed Tomography Scan in Mild Traumatic Head Injury Among Adult Patients in Mulago National Referral Hospital, Uganda: a Cross-sectional Hospital Based Study
Bibliographic record
Abstract
Abstract Background Computed Tomographic (CT) scanning of the head can detect acute intracranial injury and help to identify patients requiring neurosurgical intervention. The inappropriate utilization of CT scan strains meagre imaging resources especially in resource-constrained settings and risks the patients to unnecessary radiation. The Canadian CT head rule (CCHR) is a validated clinical tool used to predict mild head injury patients that will have a clinically significant intracranial injury on head CT scan. This reduces the number of requested CT scans while at the same time ensuring that those who would benefit from it are easily identified. However, this tool has not been previously applied in many low income settings where it would be very useful. Objective To determine the appropriateness of head CT scans performed among patients with mild traumatic head injury based on the Canadian CT head rule (CCHR). Methods This was a cross sectional study conducted at the emergency department of Mulago Hospital involving 259 adults clinically diagnosed with mild head injury with a head CT scan performed. They were assessed using the CCHR for a prediction of whether a head CT scan was appropriate or inappropriate. The proportion of appropriate head CT scans was obtained. The participants were followed up to assess their health status. Results The common abnormal CT scan findings were comminuted and depressed skull fractures. The proportion of appropriate head CT scans performed based on the CCHR was 70.7%. Most participants with positive CT scan findings were classified as appropriate when the CCHR was applied. 81.6% (n = 62) of the participants whose CT scans were classified as inappropriate had normal findings. There was a statistically significant association between categories of CCHR classification (appropriate vs inappropriate) and CT scan findings (normal vs neurologically insignificant). Conclusion About one-third of head CT scans performed in this study were inappropriate by applying the CCHR. Avoidance of CT scan in such patients is unlikely to miss any important injuries. Findings from the study can guide the adoption and adaptation of CCHR use in emergency departments.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".