Bibliographic record
Abstract
To the EditorBased on our earlier studies in Japan [1,2], we understand that we need to be more tolerant for the gestational weight gain in Japanese woman than ever, especially in overweight women.However, we have not well examined the influence of the maternal pre-pregnancy physique on the perinatal outcomes in the overweight and/or obesity women in Japan.To date, some pre-pregnancy body mass indexes (BMIs; kg/m 2 ) cut-offs have been examined to predict the high risk BMI levels as obesity associated with the adverse perinatal outcomes [3][4][5].For example, in Japan, the Japanese Ministry of Health, Labour and Welfare (JMHLW) guideline has defined pre-pregnancy BMI of ≥ 25 as obesity [3], while the Institute of Medicine (IOM) guideline in the USA has defined pre-pregnancy BMI of ≥ 30 as obesity associated with the increased risk of both neonatal macrosomia and cesarean delivery [4].In 2004, in addition, the World Health Organization (WHO) recommended the classification of pre-pregnancy BMI of ≥ 27.5 as obesity in Asian populations [5].To (re-)assess the optimal pre-pregnancy BMI cut-offs for obesity in Japanese women associated with the perinatal outcomes, we examined the perinatal outcomes in Japanese singleton pregnancy of prepregnancy BMI 25.0 -27.4,27.5 -29.9 and ≥ 30.0 compared with that of BMI 18.5 -24.9 as control.The protocol for this study was approved by the Ethics Committee of the Japanese Red Cross Katsushika Maternity Hospital.Informed consent concerning analysis from a retrospective database was obtained from all subjects.We reviewed the obstetric records of singleton pregnant Japanese women who delivered at our institute at ≥ 22 weeks' gestation from April 2012 through November 2016.Data were expressed as mean ± standard deviation or number (percentages).Cases and controls were compared by means of Student's t-test for continuous variables, and the X 2 or Fisher's exact test for categorical variables.Differences with P < 0.05 were
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".