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Frailty Significantly Associated with a Risk for Mid-term Outcomes in Elderly Chronic Coronary Syndrome Patients: a Prospective Study

2021· dataset· en· W6958084113 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMaceProspective cohort studyFrailty syndromeConfidence intervalAcute coronary syndromeMultivariate analysisRisk factor

Abstract

fetched live from OpenAlex

Abstract Introduction: Frailty is a condition of elderly characterized by increased vulnerability to stressful events. Frail patients are more likely to have adverse events. The purposes of this study were to define frailty in patients aged ≥ 70 years with chronic coronary syndrome (CCS) and to evaluate mortality and prognostic significance of frailty in these patients. Methods: We included 99 patients, ≥ 70 years old (mean age 74±5.3 years), with diagnosis of CCS. They were followed-up for up to 12 months. The frailty score was evaluated according to the Canadian Study of Health and Aging (CSHA). All patients were divided as frail or non-frail. The groups were compared for their characteristics and clinical outcomes. Results: Fifty patients were classified as frail, and 49 patients as non-frail. The 12-month Major Adverse Cardiac Events (MACE) rate was 69.4% in frail patients and 20% in non-frail patients. Frailty increases the risk for MACE as much as 3.48 times. Two patients died in the non-frail group and 11 patients died in the frail group. Frailty increases the risk for death as much as 6.05 times. When we compared the aforementioned risk factors by multivariate analysis, higher CSHA frailty score was associated with increased MACE and death (relative risk [RR] = 22.94, 95% confidence interval [CI] 3.33-158.19, P=0.001, for MACE; RR = 7.41, 95% CI 1.44-38.03, P=0.016, for death). Conclusion: Being a frail elderly CCS patient is associated with worse outcomes. Therefore, frailty score should be evaluated for elderly CCS patients as a prognostic marker.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.270
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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