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Record W3044374264 · doi:10.1016/j.hrcr.2020.07.011

Utility of 3-D printing for cardiac resynchronization device implantation in congenital heart disease

2020· article· en· W3044374264 on OpenAlexaff
Juliana Kanawati, Andrew Kanawati, Matthew Rowe, Habib Khan, William Chan, Raymond Yee

Bibliographic record

VenueHeartRhythm Case Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCardiac resynchronization therapyMedicineCoronary sinusCardiologyInternal medicineHeart diseaseHeart failureEjection fraction

Abstract

fetched live from OpenAlex

Three-dimensional (3D) printing is increasingly being used to visualize relationships of anatomical structures and is becoming more popular in planning cardiac procedures.1 3D printing of the coronary sinus (CS) and its branches has not previously been described. The value of 3D models could be particularly high in the planning of cardiac resynchronization therapy (CRT) device implantation where procedural difficulty is anticipated. CRT implantation can be challenging in patients with congenital heart disease and other structural heart abnormalities where the anatomical relationships are complex.

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)0.001
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 score0.176
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.045
GPT teacher head0.325
Teacher spread0.280 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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