Nonindicated Brain Computed Tomography Scans for Children
Bibliographic record
Abstract
OBJECTIVES: Clinical guidelines state brain computed tomography (CT) for atraumatic headache or first generalized afebrile, atraumatic seizure as nonindicated in neurologically normal children. We aimed to adapt 2 rigorously validated United States-based measures that examine overuse of CT in children with these conditions, and to determine whether these measures can be used in an Australian setting to determine rates of CT scanning in current practice. METHODS: Within an Australian tertiary pediatric hospital, we successfully adapted the measures from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system of the United States measures to the International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) coding system used in Australia. We conducted a retrospective audit of electronic medical record data from April 1, 2017, to March 31, 2018. Eligible patients were children aged 4 to 17 years who attended one of the hospital outpatient clinics and/or emergency department and/or were admitted to an inpatient ward. The conditions of interest were (1) atraumatic headache and (2) first generalized afebrile, atraumatic seizure. RESULTS: The measure for afebrile seizure was found to have low accuracy and low feasibility in this setting and was not tested further. The adapted measure for atraumatic headache was highly accurate in determining the encounters of interest; however, manual chart review was required to identify nonindicated brain CTs. Using this measure, 601 encounters for atraumatic headache were identified, of which 98 (16.3%) received at least 1 brain CT. We found that 14.1% of these scans were nonindicated, meaning 2% all atraumatic headache encounters received a nonindicated scan; lower than rates reported in international literature. CONCLUSIONS: Using the tool developed in this study, rigorous measurement of the overuse of CT scans in other settings may determine the reasons for the lower rates observed in this study; inform future interventions to minimize overuse; and provide safer, higher quality care to children.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".