Detecting Migraine in Patients with Mild Traumatic Brain Injury Using Three Different Headache Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Posttraumatic migraine may represent an important subtype of headache among the traumatic brain injury (TBI) population and is associated with increased recovery times. However, it is underdiagnosed in patients with mild traumatic brain injury (mTBI). This study examined the effectiveness of the self-administered Nine-Item Screener (Nine-Item Screener-SA), the Headache Impact Test- 6 (HIT-6), the 3-Item Migraine Screener, and the Rivermead Post-Concussion Questionnaire (RPQ) at discriminating between mTBI patients with (n = 23) and without (n = 20) migraines. The Nine-Item Screener demonstrated significant differences between migraine patients with and without migraine on nearly every question, especially on Question 9 (disability), sensitivity: 0.95 and specificity: 0.65 (95% CI, 0.64-0.90). The HIT-6 demonstrated significant differences between migraine and no-migraine patients on disability and pain severity, with disability having a sensitivity of 0.70 and specificity of 0.75 (95% CI, 0.54-0.83). Only Question 3 of the 3-Item ID Migraine Screener (photosensitivity) showed significant differences between migraine and no-migraine patients, sensitivity: 0.84 and specificity: 0.55 (CI, 0.52-0.82). The RPQ did not reveal greater symptoms in migraine patients compared with those without. Among headache measures, the Nine-Item Screener-SA best differentiated between mTBI patients with and without migraine. Disability may best identify migraine sufferers among the TBI population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it