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Record W2312344204 · doi:10.3148/cjdpr-2014-024

A Nutrition Screening Form for Female Infertility Patients

2014· article· en· W2312344204 on OpenAlexvenueno aff
Susie Langley

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2014
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsMedicineDietingInfertilityFertilityBody mass indexReferralFemale infertilityLimitingObesityGerontologyPhysical therapyEnvironmental healthGynecologyFamily medicineWeight lossPregnancyPopulationInternal medicine

Abstract

fetched live from OpenAlex

A Nutrition Screening Form (NSF) was designed to identify lifestyle risk factors that negatively impact fertility and to provide a descriptive profile of 300 female infertility patients in a private urban infertility clinic. The NSF was mailed to all new patients prior to the initial physician's visit and self-reported data were assessed using specific criteria to determine if a nutrition referral was warranted. This observational study revealed that 43% of the women had a body mass index (BMI) <20 or ≥25 kg/m(2), known risks for infertility. Almost half reported a history of "dieting" and unrealistic weight goals potentially limiting energy and essential nutrients. A high number reported eating disorders, vegetarianism, low fat or low cholesterol diets, and dietary supplement use. Fourteen percent appeared not to supplement with folic acid, 13% rated exercise as "extremely" or "very active", and 28% reported a "high" perceived level of stress. This preliminary research demonstrated that a NSF can be a useful tool to identify nutrition-related lifestyle factors that may negatively impact fertility and identified weight, BMI, diet, exercise, and stress as modifiable risk factors deserving future research. NSF information can help increase awareness among health professionals and patients about the important link between nutrition, fertility, and successful reproductive outcomes.

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)0.003
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.436
Teacher spread0.306 · 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 designOther design
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

Citations14
Published2014
Admission routes1
Has abstractyes

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