Comparison the Serum Level of Prolactin Among Patients With Chronic and Episodic Migraine
Bibliographic record
Abstract
Background: Increased serum prolactin level is suggested as one of factors for migraine to being chronic. The Aim of this study is comparison the serum level of prolactin among patients with chronic and episodic migraine. Methods: This cross-sectional study was performed from 2011 July till 2012 November among 114 female migrainous. The subjects who involved by menstrual irregularity, anxiety, depression, hypo or hypothyroidism, anemia, polycyth emia, epilepsy, and pregnant, lactating, prepuberty and postmenopause patients and hormonal preparations users didn’t entered study. The patients must avoid from any temporary affecting factors on prolactin serum level in the last 12 hours. Fasting blood was taken in the period of follicular phase and the serum level of prolactin was determined by ELISA method and compared between 2 groups. The data analysis was performed by T-test, Mann Weithney U and Qui 2 tests in SPSS 19 software. Results: A total of 57 patients with chronic migraine and 57 patients with episodic migraine with mean age of 33.0 ± 8.8 and 32.7 ± 8.7 years old respectively (P = 0.712) participated in this study. The mean serum level of prolactin was 20.92 ± 12.14 ng/dL (95% CI: 17.69 - 24.14) and 16.36 ± 9.34 ng/dL (95% CI: 13.88 - 18.84) respectively (P = 0.036). Also with omitting the age effect the difference was significant (P = 0.022). The prevalence of hyperprolactinemia in chronic and episodic migraine was 28.1% and 12.3% (P = 0.036) (OR = 2.8, 95% CI: 7.42 - 1.04). Conclusion: The prolactin serum level and the prevalence of hyperprolactinemia in chronic migrainous was higher than episodic ones. doi: http://dx.doi.org/10.4021/jnr189w
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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 | 0.000 |
| 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) | 0.002 | 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".