The usefulness of head computed tomography in patients with known cirrhosis presenting to emergency department with suspected hepatic encephalopathy
Bibliographic record
Abstract
Background: Computed tomography of the head (CT head) is frequently used for patients with cirrhosis presenting with suspected hepatic encephalopathy (HE). Aims: The primary aims of this study were to assess the frequency of CT head usage in this patient population and to determine whether these scans yielded significant findings. Our secondary aims were to identify factors associated with the decision to order CTs and whether patients who received CTs had different outcomes. Methods: A single-centre, retrospective chart review was performed. Patients presenting to the University of Alberta Hospital with cirrhosis and common liver disease aetiologies over a 27-month period were identified via discharge diagnosis codes. Charts of patients with suspected HE were manually identified. The use of a CT head was documented, as were patient demographics, cirrhosis aetiology, MELD, and outcomes. Comparisons were made between patients with and without CT head. Results: A total of 119 encounters from 100 patients met our inclusion criteria. In 57% of encounters, a CT scan was performed on presentation. None of these CT scans had significant findings. Patient factors associated with the decision to order CT included older age, more preserved liver function, and longer length of time between patient's current and previous presentations. Patients who did not receive CT head had higher in-hospital mortality, which was likely reflective of more severe underlying liver dysfunction in this group. Conclusions: The frequency of CT head usage in the studied patient population was high while the yield was low. This calls into question the usefulness of CT head in this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".