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Enregistrement W6910466240 · doi:10.48448/g86p-w039

Association of Peer Review with Completeness of Reporting, Transparency for Risk of Bias, and Spin in Diagnostic Test Accuracy Studies Published in Imaging Journals

2022· other· en· W6910466240 sur OpenAlexaboutno aff

Notice bibliographique

RevueUnderline Science Inc. · 2022
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiagnostic accuracyTransparency (behavior)Diagnostic testWilcoxon signed-rank testCompleteness (order theory)Test (biology)Medical imaging

Résumé

récupéré en direct d'OpenAlex

Objective To evaluate whether peer review of diagnostic test accuracy (DTA) studies published by imaging journals is associated with changes in completeness of reporting, transparency of risk of bias, and spin, given that there is limited evidence to support the concept that peer review improves the completeness of research reporting.1,2 Design This retrospective cross-sectional study evaluated articles published in the Journal of Magnetic Resonance Imaging (JMRI; 2019 impact factor [IF], 4.0), the Canadian Association of Radiologists Journal (CARJ; IF, 1.7), and European Radiology (EuRad; IF, 4.1) before March 31, 2020.3 Initial submitted and final versions of manuscripts were screened consecutively in reverse chronological order to include a minimum of 23 articles (based on power calculation) per journal. At least 30 eligible articles from each journal were collected when available to account for potential exclusions. Primary studies evaluating the diagnostic accuracy of an imaging test in humans were included. Studies exclusively reporting on prognostic or predictive tests were excluded. Studies were evaluated independently by 2 reviewers blinded to version for completeness of reporting using the Standards for Reporting Diagnostic Accuracy Studies (STARD) 2015 and STARD for Abstracts guidelines, transparency of reporting for risk of bias assessment based on the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), and actual and potential spin using modified published criteria. Two-tailed paired t-tests and paired Wilcoxon signed-rank tests were used for comparisons; P < .05 was considered statistically significant. Results Of 692 diagnostic accuracy studies screened, 84 articles published in 2014 to 2020 from 3 journals were included: JMRI, 30 articles; CARJ, 23; and EuRad, 31. Reporting by STARD 2015 increased between initial submissions and final accepted versions (mean reported items 16.67 vs 17.47; change, 0.80 [95% CI, 0.25 to 1.17]; P = .002). From STARD, sources of funding and other support (item 30.1) and role of funders (item 30.2) had the largest change of 0.32 (P < .001). No difference was found for the reporting of STARD for Abstracts (5.28 vs 5.25; change, −0.03; 95% CI, −0.15 to 0.11; P = .74); QUADAS-2 (6.08 vs 6.11; 0.03; 95% CI, −1.00 to 0.50; P = .92); actual spin (2.36 vs 2.40; change, 0.04; 95% CI, 0.00 to 1.00; P = .39); or potential spin practices (2.93 vs 2.81; change, −0.12; 95% CI, −1.00 to 0.00; P = .23) (Figure 20). https://assets.underline.io/uploads/markdown_image/1/image/54b0fafbe137d88e35b76a7ddac25b9a.png Conclusions This retrospective cross-sectional study found that peer review was associated with a marginal improvement in completeness of full text; however, it was not associated with abstract reporting in published imaging DTA studies nor with improvement in transparency for risk of bias assessment or reduction in spin. Considering that this study included articles from only 3 radiology journals, the findings may not be generalizable to other journals, other fields of DTA research, or non-DTA study designs. Interventions such as reviewer training and use of checklists should be evaluated. References 1. Jefferson T, Rudin M, Brodney Folse S, Davidoff F. Editorial peer review for improving the quality of reports of biomedical studies. Cochrane Library. 2020. doi:10.1002/14651858.MR000016.pub3 2. Bruce R, Chauvin A, Trinquart L, Ravaud P, Boutron I. Impact of interventions to improve the quality of peer review of biomedical journals: a systematic review and metaanalysis. BMC Medicine. 2016;14(1):1-16. doi:10.1186/ s12916-016-0631-5 3. Clarivate Analytics. Journal Citation Reports. 2019 Journal Impact Factor. Accessed July 11, 2020. https://clarivate.com/blog/announcing-the-2019-journal-citation-reports/ Conflict of Interest Disclosures Mark Schweitzer, Yves Menu, Michael Patlas, and Kelly D. Cobey have active affiliations with the 3 journals used as data sources but had no role in data extraction, analysis, or interpretation, but reviewed and approved the work. Michael Patlas reported an editorial honorarium from Springer outside of the submitted work. No other disclosures were reported. Funding/Support Funding support was received from the Philips−Radiological Society of North America research seed grant (RSNA Research & Education Foundation), Mitacs Research Training Award, and the Department of Radiology MD Summer Student Fund at the University of Ottawa. Study performance and manuscript content were the sole task and responsibility of the investigators and do not necessarily represent the official views of the funders. Role of the Funder/Sponsor The funders had no role in data collection, analysis, interpretation, or manuscript composition. Acknowledgments Sakib Kazi and Robert A. Frank contributed equally to this work. https://assets.underline.io/uploads/markdown_image/1/image/0407c7b1d6b20790b2200ea4ae9cd9e1.png

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,658
score de la tête « metaresearch » (Gemma)0,907
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,342
Score d'incertitude au seuil0,421

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,6580,907
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0050,006
Bibliométrie0,0150,021
Études des sciences et des technologies0,0050,011
Communication savante0,0150,013
Science ouverte0,0050,009
Intégrité de la recherche0,0050,004
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,106
Tête enseignante GPT0,402
Écart entre enseignants0,296 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineÉvaluation
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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