Relationship between maternal factors and preterm infant birth: a case-control study
Bibliographic record
Abstract
Literature search, G -Funds CollectionBackground.The newborn mortality rate is one of the most significant health indicators in a country.According to global data, preterm births account for 60 to 80% of all new-born fatalities caused by congenital abnormalities.Despite extensive study in developed countries, there is virtually little information on the reasons for preterm births in studies in Iran and other regions of the world.Objectives.The aim of this study was to determine the relationship between some maternal factors and preterm birth.Material and methods.This case-control study was performed on 108 mothers who had preterm births (case group) and 108 mothers who had full term births (control group).A trained midwife, through interviews, collected maternal and neonatal data from the mother and their medical records.Results.A strong relationship was reported between preterm birth and history of abortion (8.54 times), history of curettage (6.2 times), gestational diabetes (6.44 times), gestational hypertension (4.92 times), multiple gestation (5.5 times) and unwanted pregnancy (4.41 times), and an inadequate amount of prenatal care (4.81 times) was reported only in the case group. Conclusions.Based on the results, it is important to identify risk factors for preterm delivery in mothers and educate pregnant women during pregnancy.Regular and timely prenatal care helps identify mothers in high-risk groups.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".