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Record W7015592119

Um mês em um pronto-socorro de oftalmologia em Brasília

2007· article· pt· W7015592119 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2007
Typearticle
Languagept
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary health careQuarter (Canadian coin)General hospitalRetrospective cohort study
DOInot available

Abstract

fetched live from OpenAlex

OBJETIVO: Avaliar as consultas realizadas em um serviço de emergência oftalmológica em Brasília, Distrito Federal, Brasil, durante o período de um mês. MÉTODOS: Revisão retrospectiva de prontuários de pacientes atendidos no período entre 1 e 30 de setembro de 2003 no Pronto Socorro Oftalmológico do Hospital de Base de Brasília. RESULTADOS: A idade média dos pacientes foi de 32,9 ± 18,0 anos (variando entre zero e 90). Setenta por cento dos pacientes pertenciam ao grupo de população economicamente ativa (20 aos 59 anos). Sessenta e dois por cento dos pacientes atendidos pertenciam ao sexo masculino (n=1.777) e 38% ao sexo feminino (n=1.067). Dezessete por cento do pacientes atendidos procederam de outros estados, 83% possuíam endereço domiciliar no próprio Distrito Federal. Em 3% dos prontuários não constavam endereços. Dos pacientes residentes no próprio DF, 84% procederam de localidades distantes pelo menos 30 km do local da emergência. Traumas oculares de qualquer natureza foram as doenças mais freqüentes (n=730/30%), seguidos por conjuntivites (n=568/24%). Em 457 (16%) prontuários não houve qualquer preenchimento. CONCLUSÃO: O serviço público de emergência oftalmológica do DF está mal localizado. A grande maioria dos pacientes se apresenta com doenças de menor importância. Estes fatos somados ao excesso de fichas em branco demonstram que o sistema público de saúde em oftalmologia do Distrito Federal necessita de mudanças.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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.

Study designNot applicable
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

Citations0
Published2007
Admission routes1
Has abstractyes

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