P.007 Role of neuroimaging in headache management; are we following the guidelines?
Bibliographic record
Abstract
Background: Healthcare systems incur a significant financial burden through unnecessary neuroimaging, which globally, is in the order of the billions of dollars. Current recommendations suggest avoiding neuroimaging in patients with stable headaches, particularly those meeting the criteria for migraine. Methods: We conducted a retrospective chart review of 100 headache patients in an outpatient neurology clinic. We evaluated the use of CT and MRI imaging and the impact of neuroimaging on clinical management. Results: 55% of patients had a history of migraine. Overall, 74 of 100 patients had either CT or MRI imaging. Imaging was largely normal or identified non-specific, clinically irrelevant findings. There was 1 case of a cerebellopontine angle epidermoid tumor and another of suspected MS. Neuroimaging did not alter headache management. Conclusions: The data is consistent with current guidelines suggesting that neuroimaging is not necessary in patients with stable headaches, particularly migraine. Neuroimaging overuse might reflect lack of awareness of guideline recommendations, insecurity over diagnoses, medicolegal concerns, as well as patients and primary practitioners’ expectations. Resources to help improve public and physician awareness regarding neuroimaging use in patients with stable headache may help reduce unwarranted imaging studies and could have significant financial savings for healthcare systems.
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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 | 0.003 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".