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Record W2612758757 · doi:10.1161/strokeaha.117.017392

Resistant Atherosclerosis

2017· article· en· W2612758757 on OpenAlexaff
J. David Spence, Karla Solo

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsWestern University
FundersPfizerBristol-Myers Squibb
KeywordsMedicineInternal medicineCardiology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Recent studies indicate that patients with lower levels of low-density lipoprotein cholesterol (LDL-C) have greater regression of coronary plaque. In 2002, we found that carotid plaque progression doubled cardiovascular risk. In 2003, we therefore implemented a new approach, treating arteries instead of risk factors. Since then, we have seen many patients with carotid plaque progression despite very low levels of LDL-C, suggesting other causes of atherosclerosis. We studied the relationship of achieved LDL-C and change in LDL-C to progression/regression of atherosclerosis, before and after 2003. METHODS: All 4512 patients in our clinic database with at least 2 measurements of LDL-C and carotid total plaque area approximately a year apart and complete data for analyses (n=2025 before and 2487 after December 31, 2003) were included in the study. RESULTS: <0.0001). Many patients with LDL-C <1.8 mm had plaque progression (47.5%), and change in LDL-C was not correlated with plaque progression/regression. Increasing age and serum creatinine contributed to resistant atherosclerosis. CONCLUSIONS: Many patients have Resistant Atherosclerosis, failing to achieve regression of atherosclerosis despite low levels of LDL-C. Instead of relying on LDL-C, measuring plaque burden may be a more useful way of assessing individual response to therapy, particularly in resistant atherosclerosis.

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 score0.860
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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

Citations45
Published2017
Admission routes1
Has abstractyes

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