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Record W2345246055 · doi:10.1136/heartasia-2015-010660

A mechanical heart valve is the best choice

2016· article· en· W2345246055 on OpenAlexaff
Iqbal Jaffer, Richard Whitlock

Bibliographic record

VenueHeart Asia · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsPopulation Health Research InstituteMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicinevalvular heart diseaseContext (archaeology)Mechanical heart-valveAtrial fibrillationStenosisProsthesisThrombusPharmacistMechanical valveMitral valve replacementMechanical heartCardiologyIntensive care medicineHeart valveMitral valveInternal medicineSurgeryFamily medicine

Abstract

fetched live from OpenAlex

The choice of prosthesis type in patients with valvular heart disease should always be individualised. The treating heart team must weigh the concerns surrounding durability of bioprosthetic valves compared with mechanical valves and the need for lifelong anticoagulation required with mechanical valves. In general, guidelines recommend that patients under the age of 60 would benefit from a mechanical valve, and those over 70 would benefit from a bioprosthetic valve. We would argue, in this context, that the most appropriate choice for this patient would be undertaking a mitral valve replacement with a mechanical prosthesis. This recommendation is based on two considerations: first, there is a high likelihood of failure of a bioprosthesis within an unacceptably short period of time, which would then necessitate a higher risk reoperation. Second, there is high likelihood of needing long-term anticoagulation in a patient with severe mitral stenosis due to the development of atrial fibrillation. While we do acknowledge the difficulty in managing long-term anticoagulation of patients in rural settings, there have nonetheless been significant advancements in this realm with the use of pharmacist-led thrombosis clinics and point of care international normalised ratio (INR) devices in the treatment of rural patients in low-income and middle-income countries. For these reasons, therefore, we would strongly advocate for a mechanical valve in this 44-year-old patient from a rural setting.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.002

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 head0.023
GPT teacher head0.352
Teacher spread0.329 · 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 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

Citations25
Published2016
Admission routes1
Has abstractyes

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