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Record W2754012248 · doi:10.1111/echo.13712

A quality control exercise in the echo laboratory: Reduction in inter‐observer variability in the interpretation of pulmonary hypertension

2017· article· en· W2754012248 on OpenAlexafffund
Daniel M. Patton, Atoosa Enzevaie, Andrew G. Day, Anthony Sanfilippo, Amer M. Johri

Bibliographic record

VenueEchocardiography · 2017
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsQueen's University
FundersHeart and Stroke Foundation of Canada
KeywordsMedicinePulmonary hypertensionCardiologyInternal medicineVentricular pressureClinical PracticeRadiologyBlood pressurePhysical therapy

Abstract

fetched live from OpenAlex

Background Right ventricular systolic pressure ( RVSP ) estimated by echocardiography is critical for the initial screening and follow‐up of pulmonary hypertension ( PH ). Inter‐observer variability ( IOV ) in RVSP can impact clinical decision making. This study assessed whether a simple guideline‐based teaching intervention could reduce the IOV in RVSP interpretation. Methods and Results Eleven participants in a high‐volume tertiary level echocardiography laboratory underwent an assessment of the baseline IOV in the assessment of RVSP for a series of transthoracic echocardiograms ( TTE ), depicting various degrees of PH among 8 cases each before and after a teaching intervention. The inter‐observer variance (root‐mean‐square error) decreased from 26.0 mm Hg 2 (5.1 mm Hg) at baseline to 5.8 mm Hg 2 (2.4 mm Hg) post‐teaching intervention ( P = .025). The corresponding inter‐class coefficient ( ICC ) increased from 0.89 to 0.98. Several factors relating to image acquisition and interpretation were identified as contributing to IOV in RVSP . The outcome was the development of a practical tool to mitigate these factors. Conclusions A simple structured teaching intervention successfully reduced IOV in the measurement of RVSP in a high‐volume echo laboratory.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.312
Teacher spread0.283 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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