Migraine: Prevalence and Associated Disability Among Nigerian Undergraduates
Bibliographic record
Abstract
BACKGROUND: Studies on migraine in Nigeria are scanty while the disability associated with the disease has not been determined. We aimed at determining the lifetime prevalence of migraine and its associated disability among the students of a Nigerian university. METHOD: Using a multi-stage sampling method, undergraduates of Ambrose Alli University, Nigeria were screened with the International Headache Society (IHS) criteria. Those who satisfied the criteria for migraine were then assessed with the Migraine Disability Assessment questionnaire to ascertain the level of disability resulting from migraine in the three months preceding the study. Information was also obtained on the use of preventive and abortive medications. RESULTS: Out of the 1513 respondents screened, 145 satisfied the IHS criteria for the diagnosis of migraine giving an overall lifetime prevalence of 9.6% (females 10.3%, males 8.9%, p > 0.05). Migraine associated disability was little or none in 53.1% (males 55.2%, females 51.3%, p > 0.05), mild in 10.3% (males 11.9%, females 9.0%, p > 0.05), moderate in 20.7% (males 20.9%, females 20.5%, p > 0.05) and severe in 15.9% (males 11.9%, females 19.2%, p > 0.05). In spite of the fact that 53 (36.6%) of the migraineurs had moderate-severe disability, only 19/53 (35.8%) reported being on preventive treatment while none had ever used a triptan. CONCLUSION: Prevalence of migraine among our respondents falls within the range reported from similar studies among university students outside Africa. In spite of the high disability associated with the disease, use of effective preventive and abortive therapies is very poor.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".