MétaCan
Menu
Back to cohort
Record W2508693882 · doi:10.33233/nb.v14i2.233

Relação entre picacismo em gestantes e deficiência de micronutrientes

2016· article· pt· W2508693882 on OpenAlexaff
Luiz Carlos Carnevali Júnior

Bibliographic record

VenueNutrição Brasil · 2016
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsMedicinePica (typography)GynecologyHumanitiesArt

Abstract

fetched live from OpenAlex

O picacismo ou pica define-se como um transtorno alimentar caracterizado pela ingestão de substâncias não nutritivas, sendo as mais consumidas cubos de gelo e terra. A prática de pica é comumente relatada em mulheres gestantes, geralmente de baixo ní­vel socioeconômico, e parece estar associado í presença de anemia ferropriva, sendo as maiores prevalências encontradas em gestantes africanas. A etiologia da pica ainda não esta bem elucidada, porém têm sido descritos fatores culturais, nutricionais, emocionais e socioeconômicos. Uma vez que essa prática pode gerar resultados obstétricos indesejáveis, com sérios prejuí­zos para a gestante e o bebê, o presente trabalho tem como objetivo verificar a associação entre o transtorno e deficiências de micronutrientes, descrever a doença, verificar sua prevalência e esclarecer a necessidade de se desenvolver formas de investigação nas consultas de pré-natal, já que resultados positivos têm sido demonstrados com a suplementação de ferro durante a gestação associada í diminuição da pica. Conclui-se que a investigação dessa prática no pré-natal possibilita intervenções de tratamento precoce, contribuindo para a redução dos ní­veis de morbi-mortalidade e promovendo qualidade de vida ao binômio mãe-filho.Palavras-chave: pica, gestação, micronutrientes.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueNutrição BrasilSame topicCassava research and cyanideFrench-language works237,207