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Record W2218995396 · doi:10.1007/978-1-59745-485-8_15

Breathe Easy in Seattle

2007· book-chapter· en· W2218995396 on OpenAlexaff
JAMES KRIEGER, Tim K. Takaro, Janice C. Rabkin

Bibliographic record

VenueHumana Press eBooks · 2007
Typebook-chapter
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAsthmaMedicineEmergency departmentDisadvantagedDemographyLow incomeMortality rateEnvironmental healthGerontologyPsychiatrySocioeconomicsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Asthma is a common chronic health condition that disproportionately affects low income people and people of color. The prevalence and morbidity of asthma in the United States have increased dramatically in the past two decades and remain high (). Relative to wealthier and white populations, disadvantaged populations have higher asthma prevalence and experience more severe impacts such as severe attacks leading to emergency department visits and hospitalizations (, , , , , , , , , , ). Two recent publications summarize the disproportionate asthma morbidity found among black, Native American, and some Latino populations (,). Non-Hospanic blacks and American Indians of all ages had current asthma prevalence 30% higher than non-Hispanic whites in 2002 (). The emergency department visit rate among blacks was 380% higher than that among whites, the hospitalization rate was 225% higher, and the mortality rate was 200% higher ().

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.091
GPT teacher head0.313
Teacher spread0.222 · 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
GenreOther

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

Citations3
Published2007
Admission routes1
Has abstractyes

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