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Left Ventricular Diastolic Filling and Cardiovascular Functional Capacity in Older Men

2000· article· en· W2136049147 on OpenAlexaff
Robert J. Petrella, David A. Cunningham, Donald H. Paterson

Bibliographic record

VenueExperimental Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWestern University
Fundersnot available
KeywordsVerapamilPreloadIsovolumic relaxation timeInternal medicineDiastoleMedicineCardiologyWashoutVO2 maxDiastolic functionAnaerobic exerciseHemodynamicsCalciumHeart rateBlood pressurePhysical therapy

Abstract

fetched live from OpenAlex

We investigated anaerobic threshold (< theta(L)) gas exchange kinetics and maximal oxygen uptake (VO2,max) among older men with reduced left ventricular end-diastolic filling (LVDF). Ten men (mean age, 73 years) with LVDF impairment and low fitness, but without other cardiovascular dysfunction were studied. Treatments compared to control included: 5 days, high intensity exercise training protocol; 5 days, calcium channel blockade (240 mg verapamil); 21 days, detraining/washout; and 5 days, combined treatments. Results indicated no changes in resting left ventricular systolic function with any treatment. Significant resting diastolic function changes included increased early:late flow velocity (control, 0.87; training, 1.28; verapamil, 1.32), and a decreased isovolumic relaxation time (control, 0.10 s; training, 0.08 s; verapamil, 0.08 s). The combined treatments were not additive. Sub-threshold oxygen uptake kinetics (tauVO2, s) were significantly faster following either training or verapamil (tauVO2,control, 62+/-12; tauVO2,training, 44+/-9; tauVO2,verapamil, 48+/-10) and combined treatments (tauVO2, 41+/- 8). V O2,max (ml kg(-1) min(-1)) was significantly increased (control, 21.8+/-2.2; training, 27.3+/-2.2; verapamil, 25.2+/-3.4; combined treatments, 26.9+/-2.3). Increasing ventricular preload with either exercise training or calcium channel blockade was coincident with faster tauVO2 and increased VO2,max.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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.

Study designBench or experimental
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

Citations9
Published2000
Admission routes1
Has abstractyes

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