Procalcitonin Levels in Migraine Patients
Bibliographic record
Abstract
OBJECTIVES: Migraine is a risk factor for ischemic stroke. Sterile vascular inflammation may develop during migraine attacks. This study aims to investigate procalcitonin (PCT) levels amongst migraine patients as they are important markers for infection and sepsis, but can also be found at elevated levels in various cases of inflammation. METHODS: Eighty adult migraine patients participated in our study. Patients were initially separated into two main groups; Group-1 consisted of 34 patients who had migraines during the attack period. Group-2 consisted of 46 patients during the period in-between attacks. Afterwards, patients were further divided into four subgroups based on their aura status; Group-1a Migraine without aura, 27 patients during attack period, Group-1b Migraine with aura, 7 patients during attack period, Group-2a Migraine without aura, 40 patients during the period in-between attacks, Group-2b Migraine with aura, 6 patients during the period in-between attacks. RESULTS: Average PCT levels in patients during attack periods were found to be higher than the average PCT levels of patients during the period in-between attacks. These elevated levels were determined to be statistically significant(p<0.01). Serum PCT levels of the patients with migraine without aura during the attack period were significantly higher than those of patients during the period in-between attacks(p<0.01). CONCLUSIONS: Based on significantly high levels of PCT, our results support the idea that sterile inflammation plays a role in migraine pathogenesis. Further studies are necessary to understand whether PCT is a marker for ischemic stroke risk in patients who go through frequent migraine attacks.
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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".