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Record W3005608650 · doi:10.1111/jvim.15725

ACVIM consensus statement guidelines for the diagnosis, classification, treatment, and monitoring of pulmonary hypertension in dogs

2020· article· en· W3005608650 on OpenAlex
Carol R. Reinero, Lance C. Visser, Heidi B. Kellihan, Isabelle Masseau, Elizabeth A. Rozanski, Cécile Clercx, Kurt J. Williams, Jonathan A. Abbott, Michele Borgarelli, Brian A. Scansen

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

VenueJournal of Veterinary Internal Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
FundersAmerican College of Veterinary Internal Medicine
KeywordsMedicineStatement (logic)Pulmonary hypertensionConsensus conferenceIntensive care medicineCardiologyInternal medicineLinguistics

Abstract

fetched live from OpenAlex

Pulmonary hypertension (PH), defined by increased pressure within the pulmonary vasculature, is a hemodynamic and pathophysiologic state present in a wide variety of cardiovascular, respiratory, and systemic diseases. The purpose of this consensus statement is to provide a multidisciplinary approach to guidelines for the diagnosis, classification, treatment, and monitoring of PH in dogs. Comprehensive evaluation including consideration of signalment, clinical signs, echocardiographic parameters, and results of other diagnostic tests supports the diagnosis of PH and allows identification of associated underlying conditions. Dogs with PH can be classified into the following 6 groups: group 1, pulmonary arterial hypertension; group 2, left heart disease; group 3, respiratory disease/hypoxia; group 4, pulmonary emboli/pulmonary thrombi/pulmonary thromboemboli; group 5, parasitic disease (Dirofilaria and Angiostrongylus); and group 6, disorders that are multifactorial or with unclear mechanisms. The approach to treatment of PH focuses on strategies to decrease the risk of progression, complications, or both, recommendations to target underlying diseases or factors contributing to PH, and PH-specific treatments. Dogs with PH should be monitored for improvement, static condition, or progression, and any identified underlying disorder should be addressed and monitored simultaneously.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.354
GPT teacher head0.448
Teacher spread0.093 · 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