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Record W2922591415 · doi:10.1177/2048004019835449

Patient outcomes in GuideLiner facilitated percutaneous coronary intervention stratified by the SYNTAX score: A retrospective analysis

2019· article· en· W2922591415 on OpenAlexafffund
Shuangbo Liu, Christopher J. Parr, Hannah Zhang, Basem Elbarouni, Ashish H. Shah, Malek Kass, Amir Ravandi

Bibliographic record

VenueJRSM Cardiovascular Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of Manitoba
FundersHeart and Stroke Foundation of Canada
KeywordsMedicinePercutaneous coronary interventionTIMIRetrospective cohort studyStentCohortMortality rateSurgeryInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine patient outcomes in GuideLiner facilitated percutaneous coronary intervention stratified by the SYNTAX score. DESIGN: Single centre retrospective cohort analysis. PARTICIPANTS: A total of 540 consecutive cases facilitated by GuideLiner at a single center. MAIN OUTCOME MEASURES: Successful stent delivery, in-hospital, 30 day and 1 year mortality rates stratified by SYNTAX score. RESULTS: The most common indication for GuideLiner was need for increased support for balloon or stent delivery (82%), 6% for non-coaxial guide, 9% for chronic total occlusion and 3% for selective vessel engagement. Successful stent delivery was achieved in 91% of all cases, with no complications occurred due to GuideLiner use. In-hospital, 30 day and 1 year mortality rates were 2.8%, 2.1% and 4.5%, respectively. The high SYNTAX group was associated with higher rates of initial TIMI score of 0-1; however, the final TIMI score rate of successful delivery and complications did not differ between groups. In-hospital and 1 year mortality rates were higher in the higher SYNTAX groups. CONCLUSIONS: The GuideLiner is an easy to use guide catheter extension system with high rates of success and low rates of complications, across all SYNTAX groups.

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 score0.017
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.252
Teacher spread0.242 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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