Bibliographic record
Abstract
Headache is common. The lifetime prevalence of headache in the UK general population is over 90% 1 and an estimated 46% of the population are troubled by headache in any one year. eadache accounts for 4.4% of consultations in primary care 3 and 20-30% of neurology out-patient consultations. Nearly all headache is primary, with the majority of patients suffering from tension-type headache, migraine, or both together. Secondary headaches, that is, headaches attributed to another disorder, are rare. There is no diagnostic test for primary headache. Diagnosis is made by taking a good history. The vast majority of patients with headache are managed by GPs, and only 2-3% are referred to neurology services. espite this, GPs seem to be poor at diagnosing headache. In this issue of the BJGP, Kernick et al 6 report on a large cohort of patients presenting with new onset headache in primary care in the UK. They found that 70% were not given a diagnosis; 24% were diagnosed with either migraine, tension headache, or cluster headache; and 6% were diagnosed with secondary headache. This is similar to previous research. Primary care research, such as the Landmark 7 and Spectrum studies, 8 has shown that migraine is underdiagnosed and that tension-type headache and sinus headache are overdiagnosed.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".