Physician Management and CT Ordering in the Emergency Department for Primary Headaches
Bibliographic record
Abstract
OBJECTIVE: Management of primary headache (PHA) varies across emergency departments (ED), yet there is widespread agreement that computed tomography (CT) scans are overused. This study assessed emergency physicians' (EPs) PHA management and their attitudes towards head CT ordering. METHODS: A cross-sectional study was undertaken with EPs from one Canadian center. Drivers of physicians' perceptions regarding the appropriateness of CT ordering for patients with PHA were explored. RESULTS: A total of 73 EPs (70% males; 48% with <10 years of practice) participated in the study. Most EPs (88%) did not order investigations for moderate-severe primary headaches; however, CT was the common investigation (47%) for headaches that did not improve. Computed tomography ordering was frequently motivated by the need for specialist consultation (64%) or admission (64%). A small proportion (27%) believed patients usually/frequently expected a scan. Nearly half of EPs (48%) identified patient imaging expectations/requests as a barrier to reducing CT ordering. Emergency physicians with CCFP (EM) certification were less likely to perceive CT ordering for patients with PHA as appropriate. Conversely, those who identified the possibility of missing a condition as a major barrier to limiting their CT use were more likely to perceive CT ordering for patients with PHA as appropriate. CONCLUSIONS: Emergency physicians reported consistency and evidence-based medical management. They highlighted the complexities of limiting CT ordering and both their level of training and their perceived barriers for limiting CT ordering seem to be influencing their attitudes. Further studies could elucidate these and other factors influencing their practice.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".