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Record W2907565641 · doi:10.3389/fphar.2018.01475

Animal Models in the Pathophysiology of Cystic Fibrosis

2019· review· en· W2907565641 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Pharmacology · 2019
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPathophysiologyCystic fibrosisMedicinePathologyBioinformaticsBiologyInternal medicine

Abstract

fetched live from OpenAlex

Our understanding of the multiorgan pathology of cystic fibrosis (CF) has improved impressively during the last decades, but we still lack a full comprehension of the disease progression. Animal models have greatly contributed to the elucidation of specific mechanisms involved in CF pathophysiology and the development of new therapies. Soon after the cloning of the cystic fibrosis transmembrane conductance regulator (CFTR) gene in 1989, the first mouse model was generated and this model has dominated in vivo CF research ever since. Nonetheless, the failure of murine models to mirror human disease severity in the pancreas and lung has led to the generation of larger animal models such as pigs and ferrets. The following review presents and discusses data from the current animal models used in CF research.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.392
Teacher spread0.344 · 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