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Record W2804004020 · doi:10.15253/2175-6783.2018193182

Near miss materno: fatores influenciadores e direcionamentos para redução da morbidade e mortalidade materna

2018· article· pt· W2804004020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRev Rene · 2018
Typearticle
Languagept
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePhysicsGynecology

Abstract

fetched live from OpenAlex

Objetivo: analisar as evidências científicas acerca dos fatores que influenciam os casos de near miss materno e possíveis direcionamentos para redução da morbidade e mortalidade materna. Métodos: revisão integrativa com 2.895 artigos encontrados e 17 selecionados. Resultados: os fatores que influenciam nos casos de near miss foram: atrasos nos cuidados obstétricos; despreparo da equipe de saúde; condições precárias dos serviços; disponibilidade limitada de derivados de sangue; e deficiência no pré-natal, no uso de Práticas Baseadas em Evidências e nas auditorias. Como principais direcionamentos para minimizar esses eventos, evidenciaram-se: fortalecer a rede de referência e contrarreferência; realizar capacitação profissional; melhorar a cobertura do pré-natal; e investir na infraestrutura, na gestão de processos e em auditorias clínicas. Conclusão: os fatores que influenciam os casos de near miss materno englobam desde o atraso nos cuidados até a não realização do pré-natal, cuja melhoria na gestão constitui o principal direcionamento.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.004

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 head0.055
GPT teacher head0.359
Teacher spread0.304 · 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