MétaCan
Menu
Back to cohort
Record W2092707996 · doi:10.2460/ajvr.2002.63.1320

Effects of theophylline on tracheal mucociliary clearance rates in healthy cats

2002· article· en· W2092707996 on OpenAlexaff
Marilyn E. Dunn, Susan Taylor, Cindy L. Shmon, A. A. Wilkinson, Gilbert Matte, Edyta M. Dudzic, Shauna L. Cantwell, Hugh A. Semple

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2002
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTheophyllineCATSPropofolMedicineAnesthesiaMucociliary clearanceClearance rateChemistryPharmacologyInternal medicineLung

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine tracheal mucociliary clearance rate (TMCCR) by use of a standard protocol in healthy anesthetized cats and to determine the effect of theophylline on TMCCR in healthy anesthetized cats. ANIMALS: 6 healthy cats. PROCEDURE: Cats were anesthetized with propofol, and a droplet of the radiopharmaceutical technetium Tc 99m macroaggregated albumin was placed endoscopically at the carina. Dynamic acquisition scintigraphic imaging was performed, using the larynx as the end point. The TMCCR was determined by measuring the distance the droplet traveled by frame rate. Each cat was imaged 6 times as follows: 3 times following placebo administration and 3 times following the administration of sustained release theophylline (25 mg/kg, PO). Serum theophylline concentrations were assessed during imaging to ensure therapeutic concentrations. RESULTS: The TMCCR in healthy adult cats anesthetized with propofol was 22.2 +/- 2.8 mm/min. Tracheal mucociliary clearance rate in cats receiving theophylline was 21.8 +/- 3.5 mm/min. Theophylline administration did not significantly alterTMCCR. CONCLUSIONS AND CLINICAL RELEVANCE: Theophylline has been shown to increase TMCCR in humans and dogs. In our study, we determined TMCCR in healthy anesthetized cats and found that it was not accelerated by the administration of theophylline.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.081
GPT teacher head0.439
Teacher spread0.358 · 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 designOther design
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

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Veterinary ResearchSame topicCystic Fibrosis Research AdvancesFrench-language works237,207