The Effect of Season on the Prevalence of Preeclampsia
Bibliographic record
Abstract
Background: Preeclampsia can be defined as a pregnancy-specific syndrome that a group of pathological signs and symptoms occur simultaneously without known causes. This study aimed to determine the effect of season on the prevalence of preeclampsia in pregnant women referring to Sanandaj Besat Hospital during 2013 - 2014. Methods: This descriptive study was conducted on 363 pregnant women referring to Sanandaj Besat Hospital during 2013 - 2014. Data were collected from medical records of pregnant women who were hospitalized because of preeclampsia and analyzed with STATA-11 and Chi-square test. Results: The results showed that the mean age of women diagnosed with preeclampsia was 30.5 ± 6.60 years. The prevalence of preeclampsia in urban and rural pregnant women was 10% and 4%, respectively. The incidence of preeclampsia was 30% during the winter months. There was no statistically significant relationship between the season of conception and the month of preeclampsia (P = 0.67). Conclusions: Based on the findings of this study, it can be said that the risk of preeclampsia in cold seasons is more than warmer seasons and its incidence is lower in urban multiparous women. J Clin Gynecol Obstet. 2016;5(3):81-84 doi: http://dx.doi.org/10.14740/jcgo408w Â
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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 | 0.003 |
| 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) | 0.002 | 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".