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A Multicenter, Prospective Study Comparing Subxiphoid and Parasternal Views During Brief Echocardiography: Effect on Image Quality, Acquisition Time, and Visualized Anatomy

2022· article· en· W4206002462 on OpenAlexaff
Romolo Gaspari, Timothy Gleeson, Stephen Alerhand, William Caputo, Sara Damewood, Christopher Dicroce, Kristin Dwyer, Ryan C. Gibbons, Josh Greenstein, Justin Harvey, Michael D. Hill, Beatrice Hoffmann, Mary Kate Jordan, Benjamin Karfunkle, C Kropf, Robert Lindsay, Shawn Luo, Monika Lusiak, Ari Nalbandian, Leily Naraghi, Bret P. Nelson, L. Connor Nickels, Laura Nolting, Alexandra Nordberg, Joseph R. Pare, M. Peach, Dorcas Pinto, Powell L. Graham, Gabe Rose, Frances M. Russell, Jesse M. Schafer, Mark D. Scheatzle, Nikolai Schnittke, Marina Shpilko, Zachary Soucy, Jeffrey R. Stowell, Daniel Vryhof, Michael Gottlieb

Bibliographic record

VenueJournal of Emergency Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsDalhousie UniversitySaint John Regional Hospital
Fundersnot available
KeywordsParasternal lineMedicineInterquartile rangePSLImage qualityRadiologyCardiac imagingNuclear medicineInternal medicineCardiologyArtificial intelligenceImage (mathematics)Computer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.058
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.394
Teacher spread0.368 · 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

Citations16
Published2022
Admission routes1
Has abstractno

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