Repeat Head CT for Expectant Management of Traumatic Epidural Hematoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Guidelines regarding the role of repeated head computed tomography (CT) imaging in the nonoperative management of traumatic epidural hematomas (EDHs) do not exist. Consequently, some children may be exposed to unnecessary additional ionizing radiation. We describe the frequency, timing, and utility of reimaging of EDHs to identify patients who might avoid reimaging. METHODS: A retrospective cohort study of subjects aged 0 to 18 years with a traumatic EDH treated at a level I pediatric trauma center from 2003 to 2014. Radiographic and clinical findings, the frequency and timing of reimaging, and changes in neurologic status were compared between subjects whose management changed because of a meaningful CT scan and those whose did not. RESULTS: Of the 184 subjects who were analyzed, 19 (10%) had a meaningful CT. There was no difference in the frequency of CT scans between the meaningful CT scan and no meaningful CT groups (median 1 [interquartile range 1–2] in no meaningful CT and median 1 [interquartile range 1–2] in meaningful CT scans; P = .7). Only 7% of repeated CTs changed management. Neurologic status immediately before the repeat scan (odds ratio 45; 95% confidence interval 10–200) and mass effect on the initial CT (odds ratio 4; 95% confidence interval 1.5–13) were associated with a meaningful CT. Reimaging only subjects with concerning pre-CT neurologic findings or mass effect on initial CT would have decreased imaging by 54%. CONCLUSIONS: Reimaging is common, but rarely changes management. Limiting reimaging to patients with concerning neurologic findings or mass effect on initial evaluation could reduce imaging by >50%.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".