Bibliographic record
Abstract
BACKGROUND: In order to examine the optimal weight gain during pregnancy in Japanese women, we analyzed the perinatal outcomes in Japanese women with the optimal range of weight gain during pregnancy according to the Japanese (the Japanese Ministry of Health, Labour and Welfare: JMHLW and the Japan Society for the Study of Obesity: JASSO) guidelines compared with those according to the USA (the Institute of Medicine: IOM) guideline. METHODS: We compared the obstetric outcomes in two groups of gestational weight gain within the optimal range based on the IOM and Japanese guidelines in women of pre-pregnancy body mass index (BMI) categories of underweight, normal, overweight and obese. RESULTS: In the underweight and normal-weight women, the incidences of preterm delivery and low-birth-weight infant in the JMHLW group were significantly higher than those in the IOM group; however, the incidence of some other perinatal complications in the JMHLW group was significantly lower than that in the IOM group. In the overweight women, the incidences of preterm delivery and low-birth-weight infant in the JSSO group were significantly higher than those in the IOM group; however, there were no significant differences in the obstetric outcomes between the obese women in the JSSO and IOM groups. CONCLUSION: Based on the current results, we should be more tolerant for the weight gain during pregnancy in Japanese woman than ever, especially in overweight women.
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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) | 0.000 | 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".