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Record W2795753731 · doi:10.21037/jtd.2018.03.57

Does the hybrid algorithm has real impact on long-term outcomes or should only be used as a valuable approach for CTO crossing?

2018· letter· en· W2795753731 on OpenAlexfundno aff
Péter Tajti, Emmanouil S. Brilakis

Bibliographic record

VenueJournal of Thoracic Disease · 2018
Typeletter
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
FundersMedicureAbbott VascularBoston Scientific CorporationAmgen
KeywordsTerm (time)MedicineAlgorithmData miningComputer science

Abstract

fetched live from OpenAlex

The hybrid algorithm to chronic total occlusion (CTO) percutaneous coronary intervention (PCI) ( Figure 1 ) was published in 2012 and provided a systematic, angiographybased, approach to crossing coronary CTOs in 4 steps: (I) dual coronary angiography, which is essential to determine the characteristics of the lesion, especially occlusion length and the presence of collaterals appropriate for the retrograde approach; (II) systematic review of 4 lesions characteristics (proximal cap, lesion length, quality of distal vessel, and presence of interventional collaterals); (III) initial crossing strategy selection based on the aforementioned 4 parameters; and (IV) early change if the initially selected crossing strategy fails to achieve crossing within a reasonable period of time (1).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.110
GPT teacher head0.438
Teacher spread0.328 · 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
GenreCommentary

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

Citations3
Published2018
Admission routes1
Has abstractyes

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