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Record W2411171898 · doi:10.1155/2016/6150976

Preconception Care: A New Standard of Care within Maternal Health Services

2016· review· en· W2411171898 on OpenAlexaff
Stephen J. Genuis

Bibliographic record

VenueBioMed Research International · 2016
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineEtiologyHealth careDiseasePsychiatryEnvironmental healthFamily medicinePathology

Abstract

fetched live from OpenAlex

Emerging research suggests that much pediatric affliction has origins in the vulnerable phase of fetal development. Prenatal factors including deficiency of various nutrients and exposure to assorted toxicants are major etiological determinants of myriad obstetrical complications, pediatric chronic diseases, and perhaps some genetic mutations. With recent recognition that modifiable environmental determinants, rather than genetic predestination, are the etiological source of most chronic illness, modification of environmental factors prior to conception offers the possibility of precluding various mental and physical health conditions. Environmental and lifestyle modification through informed patient choice is possible but evidence confirms that, with little to no training in clinical nutrition, toxicology, or environmental exposures, most clinicians are ill-equipped to counsel patients about this important area. With the totality of available scientific evidence that now exists on the potential to modify disease-causing gestational determinants, failure to take necessary precautionary action may render members of the medical community collectively and individually culpable for preventable illness in children. We advocate for environmental health education of maternity health professionals and the widespread adoption and implementation of preconception care. This will necessitate the translation of emerging knowledge from recent research literature, to health professionals, to reproductive-aged women, and to society at large.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.070
GPT teacher head0.461
Teacher spread0.392 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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".

Quick stats

Citations31
Published2016
Admission routes1
Has abstractyes

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