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Restrictive measure for the commercialization of antimicrobials in Brazil: results achieved

2019· article· en· W2969844442 on OpenAlexaff
Josiane Moreira da Costa, Cristiano Soares de Moura, Cristiane Aparecida Menezes de Pádua, Aline Siqueira Fogal Vegi, Sérgia Maria Starling Magalhães, Marina Barra Rodrigues, Andréia Queiróz Ribeiro

Bibliographic record

VenueRevista de Saúde Pública · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMcGill University
Fundersnot available
KeywordsCommercializationMeasure (data warehouse)MedicineBusinessComputer scienceData miningMarketing

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess whether the incidence of hospital infection by a resistant microorganism decreased after the implementation of the restrictive measure of the National Health Surveillance Agency for the commercialization of antimicrobials. METHODS: A historical cohort study of medical records of adult patients admitted to a general and public hospital from May 2010 to July 2011. A cohort was formed with patients admitted in the period before the restrictive measure for the commercialization of antimicrobials (Phase I) and a second cohort was formed with patients admitted after the implementation of the restrictive measure (Phase II). RESULTS: The instantaneous risk of hospital infection by a resistant microorganism was estimated at seven by 1,000 people-time (95%CI 0.006-0.008) in Phase I, and four by 1,000 people-time (95%CI 0.003-0.005) in Phase II of the study. The differences between the survival curves in the different phases of the study and stratified by age group were also significant (p < 0.05). CONCLUSIONS: The results suggest that the implementation of the restrictive measure of the commercialization of antimicrobials by the National Health Surveillance Agency reduced the incidence of hospital infection by a resistant microorganism.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2019
Admission routes1
Has abstractyes

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