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Record W2189198699 · doi:10.18553/jmcp.2003.9.4.353

The Economic Impact of Acute Exacerbations of Chronic Bronchitis in the United States and Canada: A Literature Review

2003· review· en· W2189198699 on OpenAlexaboutno aff
Michael T. Halpern, Mitchell K. Higashi, A. W. Bakst, Jordana K. Schmier

Bibliographic record

VenueJournal of Managed Care Pharmacy · 2003
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersGlaxoSmithKline
KeywordsChronic bronchitisMedicineBronchitisIntensive care medicinePublic healthMEDLINEHealth careHealth economicsPharmacoeconomicsInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Acute exacerbations of chronic bronchitis (AECB) are recurrent and potentially severe medical events for the 13 million people in the United States who have chronic bronchitis. Medical resource use associated with AECB can have a substantial economic impact on the patients, health care system, and society overall. OBJECTIVE: To evaluate literature on the economic impact of AECB in terms of cost of illness, cost of treatments, and cost-effectiveness. METHODS: A MEDLINE literature search was conducted for studies of chronic bronchitis and costs. Reference lists of identified articles were also retrieved for review. RESULTS: Eight published studies were identified: 2 cost-of-illness studies, 1 comparative cost study, and 5 cost-effectiveness studies. Important drivers of costs associated with AECB include hospitalization and choice of antibiotics. In mild to moderate AECB, patient adherence with therapy is essential to consider when selecting treatment. The antibiotic with the lowest acquisition cost has not been shown to be the most cost effective, as adherence and clinical outcomes, particularly rehospitalization rates, differ. CONCLUSION: Further research in these areas is needed to guide clinical decision making and the conduct of disease management programs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.759
Threshold uncertainty score0.992

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 integrity0.0000.001
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 head0.013
GPT teacher head0.320
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2003
Admission routes1
Has abstractyes

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