A Headache Diagnosis Project
Bibliographic record
Abstract
BACKGROUND: Despite the availability of objective criteria, the diagnosis of migraine is thought to be missed frequently in primary practice. OBJECTIVE: To determine the most important questions assisting in the clinical diagnosis of migraine headache. METHODS: A cohort of 461 patients referred to headache specialists in Canada was assessed using a pro-forma questionnaire that was completed by the patients alone or administered by the physicians themselves. A final clinical diagnosis was recorded after a complete clinical evaluation. In a subsequent validation study, three questions derived from the results of the first phase of the study were administered to a new cohort of 128 patients, and diagnoses of "migraine" or "not migraine" were recorded according to the decision generated in the first part of the study. The final clinical diagnosis was taken as the "gold standard" for diagnosis, and the results from the two independently derived diagnostic methods were compared. RESULTS: Statistical analysis of the responses from part 1 of the study yielded three questions (related to daily occurrence, unilaterally, and functional impairment) that distinguished between pure migraine and other headache diagnoses with high reliability and validity. The sensitivity and selectivity of the three-question protocol exceeded 91%. CONCLUSIONS: The use of three questions related to headache frequency, laterality, and impact on functioning may represent an attractive screening instrument in primary care practice, alerting physicians to the diagnosis of migraine in patients or to the possibility of a second or alternative headache diagnosis in patients in whom their diagnosis of migraine previously has been made. The presence of multiple headache syndromes in individual patients, as is common in tertiary referral practice, may reduce the discriminating power of the three-question protocol.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.225 | 0.094 |
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".