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Record W2796570698 · doi:10.1016/j.rbms.2018.03.001

Herbal fertility treatments used in North America from colonial times to 1900, and their potential for improving the success rate of assisted reproductive technology

2018· article· en· W2796570698 on OpenAlexaff
Cheryl Lans, Lisa Taylor‐Swanson, Rachel Westfall

Bibliographic record

VenueReproductive Biomedicine & Society Online · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
Fundersnot available
KeywordsMiscarriageFertilityEthnobotanyAssisted reproductive technologyTraditional medicineMedicineInfertilityPhytochemicalAlternative medicineMedicinal plantsPromotion (chess)PregnancyBiologyEnvironmental healthPolitical sciencePopulation

Abstract

fetched live from OpenAlex

This paper serves to fill a gap in the literature regarding evidence for the use of botanical remedies in the promotion of fertility. It examines the botanical remedies that were used in North America (1492-1900) for all stages of reproduction from preconception to birth, and discusses their potential for future use with present-day infertility treatments. Each medicinal plant discussed in this paper is assessed using an ethnomedicinal methodology that entails examining the published ethnobotanical, phytochemical and pharmacological data. A few clinical trials have shown that there is potential for medicinal plants to improve the success rate of assisted reproductive technology (ART) treatment if used in an integrated manner, similar to the integrated use of traditional Chinese medicine with ART treatment. For example, research has shown that older women who become pregnant have a high miscarriage rate, and this is one area that complementary and alternative medicines can address.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.287
Teacher spread0.251 · 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.

Study designQualitative
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

Citations26
Published2018
Admission routes1
Has abstractno

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