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Record W2120070730 · doi:10.1136/heartjnl-2014-305837

Parathyroid hormone is associated with the LV mass after aortic valve replacement

2014· article· en· W2120070730 on OpenAlexafffund
Marie-Hélène Laflamme, Haïfa Mahjoub, Ablajan Mahmut, Marie‐Chloé Boulanger, Éric Larose, Philippe Pîbarot, Patrick Mathieu

Bibliographic record

VenueHeart · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMedicineInternal medicineParathyroid hormoneCardiologyLeft ventricular hypertrophyRenal functionAortic valve replacementEndocrinologyBlood pressureCalciumStenosis

Abstract

fetched live from OpenAlex

AIMS: LV hypertrophy (LVH) is frequent after aortic valve replacement (AVR) and is often associated with comorbidities, including hypertension, obesity, renal failure and prosthesis-patient mismatch (PPM). However, whether other biological mechanism(s) may participate to LVH after AVR is still unknown. Parathyroid hormone (PTH) may play a role in LVH. However, it is presently unknown whether PTH is associated with LVH in patients that have undergone an AVR. METHODS: In this cross-sectional study, 195 patients have been investigated at a mean of 8 ± 3.5 years following AVR. LV function and mass were evaluated by Doppler echocardiography. The plasma levels of PTH, 25-hydroxyvitamin D (25-OHD), calcium and phosphate were measured. RESULTS: There were 102 (52%) patients with LVH after AVR. In univariate analyses, PTH blood level was associated with LV mass (LVMi) and LVH. After adjustment for other risk factors, elevated PTH remained associated with LVMi (p=0.003) and LVH (p=0.02). In turn, the blood levels of 25-OHD and the renal function (GFR) were independently and inversely related to the blood level of PTH. CONCLUSIONS: After AVR, the level of PTH is independently associated with LVH. In turn, the level of PTH is related with the renal function and the level of 25-OHD.

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 score0.116
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

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