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Record W3083846749 · doi:10.1183/23120541.00304-2020

ERS International Congress, Madrid, 2019: highlights from the Pulmonary Vascular Diseases Assembly

2020· review· en· W3083846749 on OpenAlexaff
Sheila Ramjug, Jason Weatherald, Sandeep Sahay, Johad Khoury, Vasile Foris, Chandran Nagaraj, Aleksandar Bokan, Laurent Godinas, Marion Delcroix

Bibliographic record

VenueERJ Open Research · 2020
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicinePulmonary hypertensionPulmonary embolismRisk stratificationIntensive care medicineChronic thromboembolic pulmonary hypertensionInterstitial lung diseaseDiseaseLung diseaseLungCardiologyInternal medicine

Abstract

fetched live from OpenAlex

The 2019 European Respiratory Society (ERS) International Congress, held in Madrid, Spain, had exciting sessions regarding the field of pulmonary vascular disease. The symposia related to the new ERS/European Society of Cardiology (ESC) Guidelines for the diagnosis and management of acute pulmonary embolism were well received, as were sessions on pulmonary hypertension related to lung disease, demonstrating the concept of pulmonary hypertension not being the rarity that it was previously thought to be. The use of risk stratification in relation to pulmonary arterial hypertension (PAH) was heavily featured and the scientific sessions informing the respiratory community of potential biomarkers and targets for future therapies were thought-provoking. This article discusses highlights of the 2019 pulmonary vascular disease sessions as a summary of current knowledge and practice. We have summarised the key points from the sessions pertaining to the new ERS/ESC Guidelines for the management of acute pulmonary embolism. We have also focused on prognostic factors and potential therapies in pulmonary hypertension related to interstitial lung disease. Relating to PAH, we have reviewed the symposia on risk stratification, along with the use of noninvasive measures and the sessions relating to biomarkers in PAH.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0040.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.007

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 head0.153
GPT teacher head0.448
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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