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Record W2524250300 · doi:10.14740/jocmr2723w

Optimal Weight Gain During Pregnancy in Japanese Women

2016· article· en· W2524250300 on OpenAlexvenueno aff
Shunji Suzuki

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregnancyObstetricsWeight gainGynecologyBody weightInternal medicineGenetics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.149
GPT teacher head0.510
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2016
Admission routes1
Has abstractyes

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