MétaCan
Menu
Back to cohort

Preload Stress Echocardiography

2017· letter· en· W2984760117 on OpenAlexafffund
Philippe Pîbarot, Marie‐Annick Clavel

Bibliographic record

VenueCirculation Cardiovascular Imaging · 2017
Typeletter
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsMedicinePreloadCardiologyStress EchocardiographyInternal medicineHemodynamicsCoronary artery disease

Abstract

Cardiovascular Imaging, Kusunose et al, present an elegant study in which they make
\nthe first proof of concept that preload stress echocardiography
\nperformed with the use of a leg massage machine may successfully increase transvalvular flow rate and therefore differentiate severe from nonsevere AS in patients with LGAS and
\npreserved LVEF.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: editorial/commentary
about Canada: no
confidence: high

Letter commenting on a clinical proof-of-concept echocardiography study; object is a clinical technique.

GPT-5.6 (high)OUT
genre: editorial/commentary
about Canada: no
confidence: high

This letter comments on a clinical echocardiography study rather than studying research.

Grok 4.5OUT
genre: editorial/commentary
about Canada: no
confidence: high

Clinical letter on preload stress echocardiography as a diagnostic technique, not metaresearch.

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)0.001
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0020.003

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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCirculation Cardiovascular ImagingSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207