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Record W6925025436 · doi:10.17605/osf.io/k5nup

Does dietitian involvement during pregnancy improve birth outcomes? A systematic review

2022· other· en· W6925025436 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
FieldComputer Science
TopicNumerical Methods and Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyPsychological interventionPrenatal careLow birth weightMedical nutrition therapyIntervention (counseling)Incidence (geometry)Health care

Abstract

fetched live from OpenAlex

Healthy pregnancies can be achieved through sufficient weight gain, balanced diets, appropriate vitamin and mineral supplementation, avoidance of alcohol and harmful substances, as well as food safety (Procter et al., 2014). While most prenatal care providers do not have substantial training in nutrition, we believe that registered dietitians being healthcare professionals, can assist pregnant patients in attaining optimal birth outcomes. In Canada and the US, registered dietitians are not required members of health care teams for prenatal care, but can be referred to for medical nutrition therapy to assist women with weight gain, hyperemesis, multiple gestations, poor dietary patterns, and chronic disease (Procter et al, 2014). Given their expertise in nutrition and the importance of nutrition during pregnancy, registered dietitians have the potential to positively influence birth outcomes. However, studies supporting their roles have not been comprehensively evaluated (Pari-Keener et al., 2020) and existing studies have been inconsistent. Some studies have supported that interventions provided by dietitians improve infant birth outcomes. Research by Vesco et al. suggests that intensive dietary intervention initiated by dietitians is associated with a decrease in prevalence of large-for-gestational age infants (9%) compared to groups receiving only one-time dietary advice (26%) (2014). Additionally, research by Crowther et al. shows a significantly low incidence of large-for-gestational age (13%) and macrosomia (10%) infants as a result of dietitian involvement compared to its control group (22% and 21%) (2005). However, other studies indicate no effect on infant or maternal outcomes (Koivusalo et al., 2016; Dodd, Deussen & Louise, 2019). So far, no systematic review has directly studied the effect of dietitian involvement on birth outcomes. Thus, this review will examine the extent to which dietitian involvement during pregnancy is associated with improved birth outcomes. References Crowther, C. A., Hiller, J. E., Moss, J. R., McPhee, A. J., Jeffries, W. S., Robinson, J. S., & Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group (2005). Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. The New England journal of medicine, 352(24), 2477–2486. https://doi.org/10.1056/NEJMoa042973 Dodd, J. M., Deussen, A. R., & Louise, J. (2019). A Randomised Trial to Optimise Gestational Weight Gain and Improve Maternal and Infant Health Outcomes through Antenatal Dietary, Lifestyle and Exercise Advice: The OPTIMISE Randomised Trial. Nutrients, 11(12), 2911. https://doi.org/10.3390/nu11122911 Koivusalo, S. B., Rönö, K., Klemetti, M. M., Roine, R. P., Lindström, J., Erkkola, M., Kaaja, R. J., Pöyhönen-Alho, M., Tiitinen, A., Huvinen, E., Andersson, S., Laivuori, H., Valkama, A., Meinilä, J., Kautiainen, H., Eriksson, J. G., & Stach-Lempinen, B. (2016). Gestational Diabetes Mellitus Can Be Prevented by Lifestyle Intervention: The Finnish Gestational Diabetes Prevention Study (RADIEL): A Randomized Controlled Trial. Diabetes care, 39(1), 24–30. https://doi.org/10.2337/dc15-0511 Pari-Keener, M., Gallo, S., Stahnke, B., McDermid, J. M., Al-Nimr, R. I., Moreschi, J. M., Hakeem, R., Handu, D., & Cheng, F. W. (2020). Maternal and Infant Health Outcomes Associated with Medical Nutrition Therapy by Registered Dietitian Nutritionists in Pregnant Women with Malnutrition: An Evidence Analysis Center Systematic Review. Journal of the Academy of Nutrition and Dietetics, 120(10), 1730–1744. https://doi.org/10.1016/j.jand.2019.10.024 Procter, S. B., & Campbell, C. G. (2014). Position of the Academy of Nutrition and Dietetics: nutrition and lifestyle for a healthy pregnancy outcome. Journal of the Academy of Nutrition and Dietetics, 114(7), 1099–1103. https://doi.org/10.1016/j.jand.2014.05.005 Vesco, K. K., Karanja, N., King, J. C., Gillman, M. W., Leo, M. C., Perrin, N., McEvoy, C. T., Eckhardt, C. L., Smith, K. S., & Stevens, V. J. (2014). Efficacy of a group-based dietary intervention for limiting gestational weight gain among obese women: a randomized trial. Obesity (Silver Spring, Md.), 22(9), 1989–1996. https://doi.org/10.1002/oby.20831

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.019
GPT teacher head0.328
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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