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Cardiopulmonary Exercise Testing in Pulmonary Hypertension

2017· review· en· W2605017716 on OpenAlexaff
Jason Weatherald, Stefania Farina, Noemi Bruno, Pierantonio Laveneziana

Bibliographic record

VenueAnnals of the American Thoracic Society · 2017
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePulmonary hypertensionDynamic hyperinflationExercise intoleranceCardiologyInternal medicineIntensive care medicinePhysical therapyLungHeart failureLung volumes

Abstract

fetched live from OpenAlex

Abstract Cardiopulmonary exercise testing allows the assessment of the integrative cardiopulmonary response to exercise and is a useful tool to assess the underlying pathophysiologic mechanisms leading to exercise intolerance. Patients with pulmonary hypertension often face a considerable delay in diagnosis due to the rarity of the disease and nonspecific symptoms of dyspnea, fatigue, and exercise limitation. Cardiopulmonary exercise testing may be suggestive of pulmonary hypertension in patients with evidence of both circulatory impairment and ventilatory inefficiency. Other factors, such as mechanical ventilatory constraints from dynamic hyperinflation and peripheral muscle dysfunction, contribute to the profound dyspnea during exercise experienced by many patients with pulmonary hypertension. In patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension, several exercise variables, such as low peak V.o2, high Vd/Vt, and high V.e/V.co2, have proven to be useful in establishing the severity of functional impairment, predicting prognosis, and assessing the efficacy of interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.392
GPT teacher head0.484
Teacher spread0.091 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations116
Published2017
Admission routes1
Has abstractyes

Explore more

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