Evidence map›Paper›PMID 41955294›Full record

ArticleATS scholar2026

Improving inter-rater reliability in radiographic edema scoring in acute respiratory failure through structured training and expert feedback.

Hafiz Qurashi, Pranav Jain, Robin Joyce, Neha Singh, Kyle Inman, Alicia Rizzo, Vanessa Gipson, Jacob Sinopoli, Taaha Mirza, Rimsha Ali and 17 more

Abstract read
In one paragraph

Article in ATS scholar, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Hafiz QurashiDivision of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Pranav JainDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Robin JoyceComputer Vision Group, VeyTel, Inc., Pittsburgh, PA, United States.
Neha SinghDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Kyle InmanDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Alicia RizzoDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Vanessa GipsonInternal Medicine Residency Program, UPMC, Pittsburgh, PA, United States.
Jacob SinopoliInternal Medicine Residency Program, UPMC, Pittsburgh, PA, United States.
Taaha MirzaInternal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, PA, United States.
Rimsha AliInternal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, PA, United States.
Nima NaghshtabriziDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Hanna BobryshevaInternal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, PA, United States.
Nanditha VenkatesanInternal Medicine Residency Program, UPMC, Pittsburgh, PA, United States.
Konstantin GolubykhInternal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, PA, United States.
Niall PrendergastDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Keerthana SamanthapudiInternal Medicine Residency Program, UPMC, Pittsburgh, PA, United States.
Stephanie I MaximousDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Caitlin SchaeferDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Angela HendricksGenentech, Inc., South San Francisco, CA, United States.
John-Paul OliveriaGenentech, Inc., South San Francisco, CA, United States.
Christina RabeGenentech, Inc., South San Francisco, CA, United States.
Julia CluceruGenentech, Inc., South San Francisco, CA, United States.
Mohammadreza NegahdarGenentech, Inc., South San Francisco, CA, United States.
Bryan J McVerryAcute Lung Injury and Infection Center of Excellence, University of Pittsburgh, Pittsburgh, PA, United States.
Ellen HughesComputer Vision Group, VeyTel, Inc., Pittsburgh, PA, United States.
Seyed Mehdi NouraieDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Georgios D KitsiosDepartment of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, United States.

Funding

Administration for Strategic Preparedness and ResponseCenter for the Biomedical Advanced Research and Development Authority HHSO100201800036CGenentech, Inc.HHS
6 · The paper itself

Abstract

backgroundReproducible quantification of pulmonary edema on chest radiographs is challenging for bedside clinicians. The Radiographic Assessment of Lung Edema (RALE) score offers a structured, semi-quantitative approach with prognostic implications in acute respiratory failure (ARF), yet its clinical use is limited by the absence of standardized training.

objectiveTo determine whether a standardized RALE training program, combined with expert feedback, improves inter-rater reliability across clinician experience levels.

methodsWe analyzed 4487 chest radiographs from 864 critically ill patients with ARF enrolled in a prospective registry. Sixteen physicians (1 expert and 15 reviewers-including 5 interns, 4 residents, 3 fellows, and 3 attendings) completed a 3-phase RALE training program: (i) independent review of instructional materials, (ii) a live virtual session led by the expert, and (iii) iterative practice with expert feedback on discrepant scores. All scoring was performed using the Pulmo-Annotator platform. We assessed inter-rater reliability using intraclass correlation coefficients (ICC), Bland-Altman analysis, and Deming regression, while stratifying performance by experience level and self-reported confidence.

resultsInter-rater reliability improved following the training program, with ICC increasing from 0.89 (95% CI: 0.85-0.92) to 0.93 (95% CI: 0.90-0.95) after expert feedback. Fellows demonstrated the largest gain of ICC from 0.89 to 0.97, while interns and attendings maintained high reliability. Post-feedback analyses indicated reduced systematic bias. Residents exhibited the greatest variability and revised fewer scores post-feedback, despite equivalent exposure to feedback. Score distributions also varied by self-reported confidence, with greater variability among less experienced reviewers.

conclusionFollowing a structured training program, radiographic edema scoring using the RALE framework demonstrated excellent inter-rater reliability, which further improved with targeted expert feedback. This structured training program, which incorporated standardized instructional materials and iterative feedback, demonstrated that RALE scoring is teachable across different levels of clinical experience and can be integrated into educational curricula to enhance consistency in chest radiograph interpretation. These findings provide a foundation for future work exploring the role of RALE training in broader research and clinical applications.

Indexed as

Pulmonary EdemaRadiography, ThoracicRespiratory InsufficiencyClinical CompetenceFeedbackHumansObserver VariationProspective StudiesReproducibility of Resultschest radiographymedical educationRALE scorereproducibility of resultsstructured training

Identifiers

PMID41955294
PMCPMC13519325

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.