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Record W2994849121 · doi:10.1002/clc.23310

Transcatheter aortic valve replacement over age 90: Risks vs benefits

2019· review· en· W2994849121 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Cardiology · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineValve replacementStenosisReferralQuality of life (healthcare)Aortic valve stenosisPopulationSelection (genetic algorithm)Intensive care medicineCardiologyInternal medicineFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

As the population ages, clinicians will encounter a growing number of nonagenarians suffering from severe aortic stenosis who may be candidates for transcatheter aortic valve replacement (TAVR). By virtue of a healthy survivor effect or a referral bias, these patients may paradoxically have greater resilience and fewer comorbidities than their octogenarian counterparts. They tend to, on average, tolerate the TAVR procedure quite well with low in-hospital and 1-year mortality rates of 5.5% and 23%, respectively. Appropriate patient selection should consider individualized estimates of procedural risk, potential for functional recovery and for improved quantity and quality of life. Frailty is much more revealing than chronological age, and it can be measured by brief tools such as the Essential Frailty Toolset. Ultimately, the process of shared decision-making is paramount to ensure that the course of action is patient-centered and balances the procedure's expected risks and benefits with the nonagenarian's preferences and values.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.035
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.218
GPT teacher head0.512
Teacher spread0.295 · 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