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Record W4402024925 · doi:10.1177/03000605241271876

Elderly patients with very high plasma lipoprotein(a) concentrations and few cardiovascular consequences: a case series

2024· article· en· W4402024925 on OpenAlexafffund
Shyann Hang, Robert A. Hegele

Bibliographic record

VenueJournal of International Medical Research · 2024
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineSeries (stratigraphy)LipoproteinLipoprotein(a)Internal medicinePlasma lipoproteinCardiologyGerontologyEndocrinologyCholesterol

Abstract

fetched live from OpenAlex

Lipoprotein(a) (Lp(a)) is an atherogenic low-density lipoprotein (LDL)-like particle that is currently regarded as a non-modifiable risk factor for atherosclerotic cardiovascular disease. The number of patients detected with elevated Lp(a) concentrations has been increasing in recent years, although the implication of this finding is unclear for patients and physicians. We screened our lipid clinic database for patients aged >65 years with very high Lp(a) concentrations, which were defined as >230 nmol/L, and cardiovascular outcomes were assessed. The patients' (n = 16) mean (±standard deviation) age was 72.2 ± 7.1 years and the mean Lp(a) concentration was 313 ± 68 nmol/L. After a cumulative 129.0 patient-year follow-up (mean: 8.1 ± 4.2 years), the mean age was 80.3 ± 7.0 years. We observed a low baseline prevalence of cardiovascular events, with only two patients having a history of cardiovascular events. Furthermore, zero incident adverse cardiovascular events were recorded over the follow-up. Therefore, very high Lp(a) concentrations and disease-free old age are not mutually exclusive. Our aggregated clinical experience is that there is only a modest association between elevated Lp(a) concentrations and adverse outcomes. Nonetheless, we still advise treating modifiable risk factors in these patients.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.335
Teacher spread0.307 · 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 designOther design
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
Published2024
Admission routes2
Has abstractyes

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