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Record W2129477702 · doi:10.1164/ajrccm.161.1.9903055

Effects of Continuous Positive Airway Pressure on Cardiac Volumes in Patients with Ischemic and Dilated Cardiomyopathy

2000· article· en· W2129477702 on OpenAlexaff
Sangeeta Mehta, PETER P. LIU, Fabia S. Fitzgerald, Yasmin Allidina, T. Douglas Bradley

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineCardiologyInternal medicineContinuous positive airway pressureDilated cardiomyopathyHeart failureRadionuclide angiographyDiastoleCardiomyopathyBlood pressureEjection fraction

Abstract

fetched live from OpenAlex

The effects of continuous positive airway pressure (CPAP) on left (LV) and right ventricular (RV) volumes in patients with congestive heart failure (CHF) have not been studied. We hypothesized that CPAP would cause greater reductions in cardiac volumes in CHF patients with idiopathic dilated cardiomyopathy (IDC) than in those with ischemic cardiomyopathy (IsC), because their ventricles are more compliant. The effects of a 30-min CPAP application at 10 cm H(2)O on RV and LV end-diastolic (EDV) and end-systolic volumes (ESV), determined by radionuclide angiography, were therefore tested in 22 patients with CHF due to IsC (n = 13) or IDC (n = 9). CPAP-induced reductions in LVEDV, LVESV, RVEDV, and RVESV were significantly greater (p < 0.05) in the IDC than in the IsC group. Whereas in the IsC group CPAP caused no significant changes in LV or RV volumes, in the IDC group it induced significant reductions in RVEDV (527 +/- 77 ml to 354 +/- 50 ml, p = 0.03) and RVESV (400 +/- 78 ml to 272 +/- 54 ml, p = 0.04) that were greater than any reductions in LVEDV and LVESV. We conclude that CPAP causes greater short-term reductions in RV and LV volumes in CHF patients with IDC than in those with IsC, and that among patients with IDC, CPAP causes greater reductions in RV than in LV volumes.

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)0.000
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 score0.122
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.263
Teacher spread0.257 · 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

Citations79
Published2000
Admission routes1
Has abstractyes

Explore more

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