Prediction of Factors Affecting Cognitive Performance in Pregnant Women using Robust Regression Methods
Bibliographic record
Abstract
Introduction: This study compared the outcomes of cognitive function assessments between pregnant and non-pregnant women groups to demonstrate alterations occurring during pregnancy. Furthermore, we aimed to determine the factors acting on cognitive functions in pregnant women. Material and Methods:42 pregnant and 42 non-pregnant women were included in the study. In order to compare cognitive performances, Montreal Cognitive Assessment test was applied to women. Results:The assessed scores of cognitive functioning were significantly different between pregnant and non-pregnant women (p <0.001). The test value was obtained as 22.29±4.57 with pregnants and as 26.02±2.19 with non-pregnant womens. The cognitive measurements yielded lower scores in the pregnant women. A negative correlation was found between the progesterone hormone levels and cognitive scores (p = 0.025). Progesterone hormone, TSH hormone and age of the pregnant were found to be important among the factors affecting the cognitive performances in pregnants (p=0.04; p=0.001; p=0.033, respectively). Conclusion:Significant reductions in cognitive functions are observed in women during pregnancy. During pregnancy, in order to increase the cognitive level of women, hormonal values of pregnant women should be followed.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".