Headache Phenotypes in Idiopathic Intracranial Hypertension and Its Short-Term Outcomes: A Retrospective Case Series Study
Bibliographic record
Abstract
Background: Idiopathic intracranial hypertension (IIH) presents a complex physiopathology, leading into diverse manifestations, notably variable headache phenotypes. Furthermore, its frequent overlap with migraine complicates the evaluation of treatment benefit for IIH-related headache. Our aim was to investigate if there is any relationship between demographic factors, clinical patterns of headache, treatment response, and headache short-term outcome with the headache phenotype of IIH. Methods: This study was a retrospective analysis of demographic, clinical, and treatment features of patients with idiopathic intracranial hypertension presenting with headache and evaluation of headache outcomes in the first 12 months following treatment. Results: O). Patients presented with migraine (n = 11, 34.4%), tension-type (n = 9, 28.1%), or a not-classifiable headache (n = 12, 37.5%). Regarding treatment and short-term follow-up (12 months), there was a failure of medical treatment in 43.8% (n = 14) and a reduction of headaches (≥ 50%) in 62.5% (n = 20) of the patients. Among headache phenotypes, there were no significant differences regarding demographics, clinical features, clinical patterns, or treatment response at baseline. Also, there were no differences regarding response to treatment or headache outcomes in 1, 3, 6, and 12 months of follow-up. Conclusions: In our study, migraine and unclassifiable types were the most commonly reported headache phenotypes. Headache phenotype does not appear to be an essential factor in allowing clinical distinction, treatment response, or predicting the short-term headache outcome of this intriguing entity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".