Headache Attributed to Infection: Observations on the IHS Classification (ICHD-II)
Bibliographic record
Abstract
The aim of this study was to revise some topics in the chapter "Headache attributed to infections" in the last International Headache Society (IHS) classification. The authors searched for original studies and reviews about headache associated with infections. A checklist was submitted to 15 neurologists to quantify the relevance, comprehensibility and coherence between definitions, criteria and comments for each paragraph. The following paragraphs were fully discussed: (1) headache attributed to lymphocytic meningitis. This topic, being rather heterogeneous, should be divided into different subgroups; (2) headache attributed to HIV/AIDS. Distinctive features are not specified and diagnostic criteria are rather confusing; and (3) chronic post-infection headache. Diagnostic criteria should be reconsidered as the symptom "pain" is not the main diagnostic criterion. The authors propose the revision of three paragraphs of the new IHS classification to better define the most likely headache profile in specific CNS infections. The authors also underline the need to plan further ad hoc prospective studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".