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Record W2040007181 · doi:10.1080/02770900601182483

Asthma Mortality in Southern Brazil: Is There a Changing Trend?

2007· article· en· W2040007181 on OpenAlexaff
Gustavo Chatkin, José Miguel Chatkin, Carlos Cézar Fritscher, Daniela Cavalet-Blanco, Hélio Radke Bittencourt, Malcolm R. Sears

Bibliographic record

VenueJournal of Asthma · 2007
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's Hospital
Fundersnot available
KeywordsAsthmaMedicineDemographyMortality ratePopulationTrend analysisPediatricsEnvironmental healthSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mortality from asthma increased during the last decades but is now declining in some countries. Little is known about this trend in Brazil. OBJECTIVE: The objective of the study was to determine the trends in asthma mortality in Southern Brazil. METHODS: We reviewed death certificates of 566 people in the state of Rio Grande do Sul, Brazil, between 5 and 39 years of age in whom asthma was reported to be the underlying cause of death during the period of 1981-2003. Population data were available in 5-year age groups. Mortality rates were submitted to linear and quadratic regression procedures. RESULTS: Among children and teenagers (5-19 years), there were 170 asthma deaths, ranging from 4 to 13 deaths each year with rates of 0.154/100,000 to 0.481/100,000. In young adults (20-39 years), 396 asthma deaths occurred, ranging from 9 to 32 each year, with rates from 0.276/100,000 to 1.034/100,000. There was an initial increase in rates, with later stabilization, and then the start of a decline beginning in the late 1990s and the early part of this decade. This trend occurred in both age subgroups examined but was more evident in males. CONCLUSIONS: Asthma mortality in southern Brazil remains low and appears to be decreasing after reaching a peak in the mid-1990s. The reason for these trends remains unknown.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.312
Teacher spread0.295 · 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 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

Citations17
Published2007
Admission routes1
Has abstractyes

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