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Heart or Lungs? Uncovering the Causes of Exercise Intolerance in a Patient with Chronic Cardiopulmonary Disease

2018· article· en· W2888903697 on OpenAlexaff
Alcides Rocha, Flávio F. Arbex, Priscila A. Sperandio, Frederico Mancuso, Maria Clara N. Alencar, Aline Souza, Ligia Biazzim, Denis E. O’Donnell, J. Alberto Neder

Bibliographic record

VenueAnnals of the American Thoracic Society · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineExercise intoleranceDiseaseCardiologyIntensive care medicineInternal medicineHeart failure

Abstract

fetched live from OpenAlex

A 52-year-old woman was referred for potential mitral valve replacement because of severe stenosis and moderate regurgitation secondary to rheumatic heart disease. She denied smoking tobacco; however, she had been exposed to biomass smoke (indoor cooking) for more than 30 years. Moreover, she reported having had pulmonary tuberculosis treated with standard chemotherapy 20 years before. Her mitral stenosis had been treated with percutaneous mitral balloon commissurotomy (PMBC) 15 years ago. Her functional performance had declined in the past few years. An echocardiographic mitral valvuloplasty outcome prediction score, however, indicated poor outcome for repeated PMBC (Wilkins score = 10). The remaining alternative (prosthetic valve replacement) is associated with higher morbidity and mortality than PMBC. In this context, the following was the clinical challenge: Is this patient’s functional limitation related primarily to cardiocirculatory impairment secondary to severe mitral stenosis or, alternatively, to her respiratory comorbidities? In the first scenario, prosthetic valve replacement could still be considered to improve her symptoms. Alternatively, if coexistent respiratory impairment contributes importantly to her breathing discomfort, the procedure would not be indicated.

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)0.000
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 score0.425
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.343
Teacher spread0.313 · 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 designObservational
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

Citations4
Published2018
Admission routes1
Has abstractyes

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