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Record W1993517792 · doi:10.5402/2012/309470

Performance of a Simplified Wall Motion Score Index Method for Noncardiologists to Assess Left Ventricular Ejection Fraction

2011· article· en· W1993517792 on OpenAlexaff
Réal Lebeau, Brian J. Potter, Georgetta Sas, Sherif Moustafa, Maria Di Lorenzo, Vicky Soulières, Yannick Beaulieu, Claude Sauvé, R Amyot, Karim Serri

Bibliographic record

VenueISRN Emergency Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsEjection fractionFraction (chemistry)CardiologyMedicineInternal medicineHeart failureChemistry

Abstract

fetched live from OpenAlex

Backgrounds . For emergency physicians performing a goal-directed transthoracic echocardiogram (TTE), a reliable estimate of LVEF must be obtained rapidly. We compared rapid LVEF estimates obtained from short axis sections to those obtained from apical sections using two methods of evaluation. Methods . The TTE's of 6 patients were interpreted by 16 echo-proficient readers (PRO group) and 105 novice readers (NOV group). LVEF was assessed by each group. The strategies consisted of either a global visual estimation (VIS) of LVEF or semiquantitative (SQ) methods. Results . Using RNV and BIP as a reference standard, NOV readers performed better with the SQ method than global visual estimation. For NOV readers, best agreement was achieved with the 234C sequence in low LVEF situations, but with the BMA series in normal LVEF settings. Neither series of views was better than the other in the setting of mild LVEF depression. Conclusion . Semi-quantitative method was superior to global visual estimation of LVEF in NOV group in most of the LVEF ranges.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.169
GPT teacher head0.420
Teacher spread0.251 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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