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Record W2169778462 · doi:10.1016/j.ehj.2004.02.033

Risks and benefits of optimised medical and revascularisation therapy in elderly patients with angina ? on-treatment analysis of the TIME trial

2004· article· en· W2169778462 on OpenAlexaboutno aff
Christoph Kaiser

Bibliographic record

VenueEuropean Heart Journal · 2004
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
FundersSchweizerische HerzstiftungEuropean Society of Cardiology
KeywordsMedicineAnginaConventional PCIMedical therapyCanadian Cardiovascular SocietyRevascularizationInternal medicineCardiologySurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

AIM: To assess treatment effects of optimised medical therapy and PCI or CABG surgery on one-year outcome in patients 75 years old with chronic angina. METHODS AND RESULTS: On-treatment analysis of the TIME data: all re-vascularised patients (REVASC n=174: 112 randomised to revascularisation and 62 to drugs with late revascularisation) were compared to all patients on continued drug therapy (MED n=127: 86 randomised to drugs and 41 to revascularisation only). Baseline characteristics of both groups were similar (age 80 +/- 4 years). Risk of death at one year (adjusted hazard ratio (HR)=1.31; 95%-CI: 0.58-2.99; P=0.52) and of death/infarction (adjusted hazard RATIO=1.77; 95%-CI 0.91-3.41; P=0.09) were comparable between REVASC and MED patients. Furthermore, the risk of death within 30 days was even slightly lower among REVASC patients (unadjusted hazard RATIO=0.73; 95%-CI: 0.21-2.53; P=0.98). Overall, REVASC patients had greater improvements in symptoms and well-being than MED patients (P<0.01). Surgical patients had similar mortality rates as angioplasty patients, but they also had greater symptomatic improvements (P<0.01). CONCLUSION: Treated medically, elderly patients with chronic angina have a similarly high 30-day and one-year mortality as patients of the same age being re-vascularised; however, they can expect lower improvements in symptoms and well being.

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 score0.047
Threshold uncertainty score0.185

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 head0.041
GPT teacher head0.285
Teacher spread0.244 · 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

Citations43
Published2004
Admission routes1
Has abstractyes

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