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Record W1604912027 · doi:10.1111/jch.12647

The Comparative Effectiveness of Angiotensin‐Converting Enzyme Inhibitors and Angiotensin<scp>II</scp>Receptor Blockers in Patients With Diabetes

2015· article· en· W1604912027 on OpenAlexafffund
Raj Padwal, Lin Mu, Dean T. Eurich

Bibliographic record

VenueJournal of Clinical Hypertension · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsAlliance for Canadian Health Outcomes Research in DiabetesProvincial Laboratory of Public HealthDiabetes CanadaUniversity of Alberta
FundersCanadian Institutes of Health ResearchUniversity of AlbertaCanadian Diabetes Association
KeywordsMedicineHazard ratioInternal medicineConfidence intervalClinical endpointDiabetes mellitusAngiotensin-converting enzymeRetrospective cohort studyAngiotensin Receptor BlockersLower riskProportional hazards modelCohort studyCohortEndocrinologyClinical trialBlood pressure

Abstract

fetched live from OpenAlex

The evidence examining the effect of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) on mortality in high-risk patients is conflicting. To further examine this controversy, the authors compared outcomes between ACE inhibitors and ARBs in a large clinical diabetes registry. A retrospective cohort of 87,472 incident users followed for 105,702 patient-years was analyzed. Average age was 53.1±10.1 years, 54.2% were men, and 14.4% had cardiovascular disease. All-cause hospitalization or all-cause mortality, the composite primary endpoint, occurred in 10,943 (12.5%) patients. Compared with ACE inhibitors, the adjusted hazard for ARBs was 0.90 (95% confidence interval, 0.87-0.94) for all-cause hospitalization or mortality; 0.95 (0.65-1.40) for mortality; 0.90 (0.87-0.94) for all-cause hospitalization; and 0.81 (0.74-0.89) for cardiovascular admission. ARB use was associated with a reduced, not increased, risk of hospitalization/mortality relative to ACE inhibition. This was driven by lower hospitalization, with a null mortality result.

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)0.003
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 score0.018
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.313
Teacher spread0.261 · 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
Published2015
Admission routes2
Has abstractyes

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