[A study on the risk factors of congenital urogenital malformations].
Bibliographic record
Abstract
OBJECTIVE: To explore risk factors for congenital urogenital malformations. METHODS: A hospital-based category matching case-control study was performed to investigate the characteristics of 95 cases with congenital urogenital malformations and 160 controls. Parents were interviewed for their characteristics before and during conception. Unconditional logistic regression analysis was used for single factor and multivariate analysis to estimate odds ratios and their 95% confidence intervals. RESULTS: There were 71 male infants in the overall congenital urogenital malformations (Ratio = 74.4%, Chi(2) = 74.863, P = 0.001) identified. Statistically significant associations were calculated for the pregnant women suffering from cold during first and/or second trimester (OR = 7.034, 95% CI = 3.488 - 14.187), for a low birthweight delivery (OR = 4.075, 95% CI = 1.774 - 9.362), for maternal times of pregnancy >/= 2 (OR = 3.133, 95% CI = 1.514 - 6.483) and exposure to occupational detrimental chemicals before pregnancy (OR = 10.496, 95% CI = 1.053 - 104.651). CONCLUSIONS: The risk factors of congenital urogenital malformations were suffering from cold during first and/or second trimester, male infants, low birthweight delivery, times of pregnancy >/= 2 and exposure to occupational detrimental chemicals before pregnancy.
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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 | 0.002 |
| 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) | 0.008 | 0.001 |
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".