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Idade materna e características de recém-nascidos em óbito no período neonatal, 2000 a 2009

2012· article· pt· W2322350499 on OpenAlexaff
Rosângela Aparecida Pimenta Ferrari, María Rita Bertolozzi

Bibliographic record

VenueCiência Cuidado e Saúde · 2012
Typearticle
Languagept
FieldHealth Professions
TopicMaternal and Neonatal Healthcare
Canadian institutionsQUAD Engineering (Canada)
Fundersnot available
KeywordsMedicineBirth certificateGestational ageBirth weightApgar scorePediatricsObstetricsUnivariate analysisLow birth weightExact testPregnancyDeath certificateInfant mortalityMultivariate analysisCause of deathPopulationInternal medicineBiology

Abstract

fetched live from OpenAlex

The aim of this study was to identify the relation between maternal age and characteristics of infants who died at the neonatal period in Londrina – PR. Retrospective ecological study using data from the Birth Certificate, Death Certificate and Infant Death Investigation Form from the Municipal Committee for Maternal Infant Mortality Prevention, from 2000 to 2009. Univariate and bivariate analysis applying Qui-square and Fisher’s exact tests were performed considering p<0.05. 60.2% were young adults, and 22.0% of those were teenagers. Maternal age was statistically significant to type of delivery (p<0.01) and not significant to gestational age, birth weight, Apgar at the first and fifth minute. 58.2% of infants were born within less than 36 weeks, 50.0% weighted less than 1500 grams and 57.7% suffered from asphyxiation in the first minute. More than 70.0% of infants died at the neonatal period. Results did not present statistically significant relation between maternal age extreme groups but to the limited biological conditions of infants which are possibly related to pregnancy and delivery.

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)0.002
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.369
Teacher spread0.324 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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