Frequency of maternal high serum hs-C reactive protein level in pre-eclampsia patients.
Bibliographic record
Abstract
Objective: To determine the frequency of high serum high-sensitivity C-reactive protein (hs-CRP) level in pre-eclampsia women. Study Design: Cross-sectional study. Setting: Department of Obstetrics and Gynaecology, Bahawalpur Medical and Dental College, Bahawalpur. Period: October 2022 to February 2023. Material & Methods: A total of 206 women aged between 20-35 years with singleton pregnancy, gestational age >20 weeks, any parity, and pre-eclampsia were analyzed. High serum hs-CRP was defined as when serum hs-CRP was above 7.0mg/L. The patient was diagnosed with pre-eclampsia when she had systolic blood pressure ≥140mmHg, measured at two different intervals after 20 weeks of pregnancy and proteinuria (200 mg or more per 24-hour period) by laboratory test. The frequency of high hs-CRP was noted. Results: In a total of 206 females, the mean age was 29.69±3.12 years while 120 (58.3%) were aged between 18-30 years. The mean gestational age and parity were 27.83±2.64 weeks and 2.65±1.44 respectively. Frequency of high serum hs-CRP was noted in 135 (65.5%) females with pre-eclampsia. It was found that age (p=0.686), gestational age (0.253) and parity (p=0.927) were not having any statistically significant association with high serum hs-CRP. BMI was noted to have significant association with high serum hs-CRP (p<0.001). Conclusion: The frequency of high maternal hs-CRP was very high (65.5%) among females with pre-eclampsia.
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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".