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Record W3165893107 · doi:10.1016/j.xjtc.2021.06.008

Tailoring the Ross procedure for patients with aortic regurgitation

2021· editorial· en· W3165893107 on OpenAlexaff
Amine Mazine, Ismaı̈l El-Hamamsy

Bibliographic record

VenueJTCVS Techniques · 2021
Typeeditorial
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRegurgitation (circulation)MedicineScopusBicuspid aortic valveAortic valveAortic Valve InsufficiencyInternal medicineCardiologyRoss procedureAortic valve repairAortic rootSurgeryMEDLINEAortic valve replacementAortaStenosis

Abstract

fetched live from OpenAlex

Aortic valve repair and aortic valve-sparing operations represent the best options to treat aortic regurgitation (AR) in nonelderly adults.1,2 When performed by expert surgeons, these operations are safe and result in good durability and freedom from valve-related complications, leading to excellent long-term survival.3,4 When the aortic valve cannot be repaired or spared, valve replacement becomes essential. The vast majority of patients who undergo aortic valve replacement (AVR) will receive a bioprosthetic or mechanical valve.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.006
GPT teacher head0.327
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations11
Published2021
Admission routes1
Has abstractyes

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