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Enregistrement W3094901197 · doi:10.1016/j.jaad.2020.10.027

Fairness and transparency in medical journals

2020· letter· en· W3094901197 sur OpenAlexaboutno aff
Dirk M. Elston, Jane M. Grant‐Kels, Nikki Levin, Murad Alam, Emily Altman, Robert T. Brodell, Anthony P. Fernandez, M. Yadira Hurley, John C. Maize, Désirée Ratner, Julie V. Schaffer, Jonathan Kantor

Notice bibliographique

RevueJournal of the American Academy of Dermatology · 2020
Typeletter
Langueen
DomaineDecision Sciences
ThématiqueAcademic Publishing and Open Access
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVettingMedicineTransparency (behavior)Focus (optics)Internet privacyFamily medicinePolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

Medical journals serve the public through vetting, dissemination, and archiving of scientific information. This letter will focus on some controversial trends, including prepublication, open reviews, and double-blind review. Prepublication allows open access to data before vetting has occurred. Obvious risks include wide dissemination of biased data, but the human genome project served as a model for the legitimate use of prepublication, allowing more rapid progress of research beyond that which the producers of the data could have accomplished on their own.1Birney E. Hudson T.J. et al.Toronto International Data Release Workshop AuthorsPrepublication data sharing.Nature. 2009; 461: 168-170Crossref PubMed Scopus (197) Google Scholar At its best, prepublication allows for early dissemination of vital data. At its worst, unvetted data can cause patient harm, or important data may be lost or remain hidden if no journal accepts responsibility for its vetting, dissemination, and archiving. Standards for prepublication are being developed, including how to conform to ethical standards, how data are made available, how investigators should cite the original source, and how data should be archived. Funding agencies may help determine which data sets have broad utility that mandate rapid prepublication release. Peer review remains the criterion standard among medical journals, but it can be difficult to balance anonymity and transparency. Most journals do everything possible to protect reviewer confidentiality to ensure candid reviews, but some journals have adopted an open process where reviewer identity is known and the reviews themselves are widely available to public scrutiny or published with the article. Open reviews have been promoted as a means of allowing open discourse about limitations in study design and giving credit to reviewers for an important but often thankless job. The negative effects of open reviews include hesitation to disagree with prominent authors and difficulty in finding reviewers who are willing to be candid without protection of their identity.2Vercellini P. Buggio L. Viganò P. Somigliana E. Peer review in medical journals: beyond quality of reports towards transparency and public scrutiny of the process.Eur J Intern Med. 2016; 31: 15-19Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar Although some favor open reviews, other journals have gone in the opposite direction with double-blinded reviews. The major advantage of double-blinded review is avoidance of a perception of bias. Disadvantages include reduced ability to determine author expertise, conflict of interest, duplicate publication, or salami-slicing of data sets. Published evidence suggests that when selecting presenters at national meetings, single-blinding favors prestigious speakers,3Tomkins A. Zhang M. Heavlin W.D. Single- vs. double-blind reviewing at WSDM 2017.Proc Natl Acad Sci U S A. 2017; 114: 12708-12713Crossref PubMed Scopus (218) Google Scholar but there is less evidence to suggest a benefit to double-blinding of journal reviews. Double-blind review is often not truly blinded, because reviewers can commonly identify the authors by other means.4Saini J.R. Sonthalia N.R. Dodiya K.A. Identification of author and reviewer from single and double blind paper.World Acad Sci Eng Technol. 2014; 8: 143-147Google Scholar In the case of one dermatology journal, blinding during peer review did not appear to affect the disposition of the manuscript, and there was no difference in word count between blinded and unblinded reviews.5Alam M. Kim N.A. Havey J. et al.Blinded vs. unblinded peer review of manuscripts submitted to a dermatology journal: a randomized multi-rater study.Br J Dermatol. 2011; 165: 563-567Crossref PubMed Scopus (40) Google Scholar Data also suggest that double-blind peer reviews do not result in higher rates of female authorship. On the contrary, although female authorship has increased across all journals, it decreased in double-blind while increasing in single-blind journals.6Cox A.R. Montgomerie R. The cases for and against double-blind reviews.PeerJ. 2019; 7: e6702Crossref PubMed Scopus (16) Google Scholar Acceptance rates are lower and reviews are more critical with double-blind review, and these patterns are the same for female and male authors.7Blank R.M. The effects of double-blind versus single-blind reviewing: experimental evidence from The American Economic Review.Am Econ Rev. 1991; 81: 1041-1067Google Scholar Given these factors, the majority of journals have retained single-blinded review. Journal of the American Academy of Dermatology allows authors to recommend experts in the field as possible reviewers and enumerate reviewers whom they believe could be biased against their work and therefore should be avoided as reviewers for a particular article. We have always honored the latter request. We consider requests for double-blinded review on a case-by-case basis when there is a high likelihood of bias and publish commentary when reviews suggest important limitations in research methods.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Science ouverte, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,344
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0030,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,002
Communication savante0,0000,001
Science ouverte0,0090,001
Intégrité de la recherche0,0010,012
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,089
Tête enseignante GPT0,425
Écart entre enseignants0,336 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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