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Record W2145021783 · doi:10.1586/ers.10.68

Animal models of cigarette smoke-induced chronic obstructive pulmonary disease

2010· article· en· W2145021783 on OpenAlexafffund
Andrew Churg

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineChronic bronchitisCOPDCigarette smokeAirwaySmokeDiseaseAsthmaPulmonary diseaseParenchymaBronchitisPathologyIntensive care medicineImmunologyInternal medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

Chronic exposure of laboratory animals to cigarette smoke reproduces many of the anatomic/physiologic lesions (emphysema, small-airway remodeling and pulmonary hypertension) of human chronic obstructive pulmonary disease, although smoke-exposed laboratory animals are not good models of chronic bronchitis or acute exacerbations, as these are conditions based upon symptoms that are not recapitulated in animals. Many types of antiproteolytic and anti-inflammatory interventions, such as use of drugs or genetic modifications, are highly effective in preventing emphysema in these models, and some also prevent small-airway remodeling and pulmonary hypertension. However, the few attempts to translate these therapies into humans have been unsuccessful, probably because the animal models typically start therapy from day 1 of smoke exposure, whereas most humans are treated late in the course of their disease. Recent data from our laboratory suggest that the parenchyma can repair smoke-induced damage for some period, but then switches to a mode where it fails to repair; these observations suggest that the timing of an intervention in humans may be crucial to its success. The various different anatomic lesions induced by smoke appear to be largely independent effects and may require different therapeutic approaches.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.358
Teacher spread0.313 · 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 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

Citations66
Published2010
Admission routes2
Has abstractyes

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