Adherence of SARS-CoV-2 seroepidemiologic studies to the ROSES-S reporting guideline during the COVID-19 pandemic
Notice bibliographique
Résumé
Abstract Background Complete reporting of seroepidemiologic studies (e.g. sampling and measurement methods, immunoassay characteristics) are critical to their interpretation, comparison, and utility in evidence synthesis. The Reporting of Seroepidemiologic studies—SARS_JCoV_J2 (ROSES-S) guideline is a reporting checklist that aims to improve the quality and transparency of reporting in SARS-CoV-2 seroepidemiological studies. While the synthesis of seroepidemiologic studies played a crucial role in public health decision-making during the COVID-19 pandemic, adherence of SARS-CoV-2 seroepidemiologic studies to the ROSES-S guideline has not yet been evaluated. Objectives To evaluate the completeness of SARS-CoV-2 seroepidemiologic study reporting over the first two years of the COVID-19 pandemic by assessing adherence to the ROSES-S reporting guideline, determine whether publication of the ROSES-S guideline was associated with changes in reporting completeness, and identify study characteristics associated with reporting completeness. Methods A stratified random sample of SARS-CoV-2 seroepidemiologic studies from the SeroTracker living systematic review database was evaluated for adherence to the ROSES-S guideline. We categorized study adherence to each reporting item in the guideline as “reported”, “not reported”, or “not applicable”. For each reporting item we calculated the percentage of studies that were adherent. We also calculated the median and interquartile range (IQR) adherence across all items and by item domain. Piecewise and multivariable beta regression analyses were used to determine whether publication date of the ROSES-S guideline was associated with changes in the overall adherence scores and to identify study characteristics associated with overall adherence scores. Results 199 studies were included and analyzed. The median adherence to reporting items was 48.1% (IQR 40.0%–55.2%) per study. Adherence to reporting items ranged from 8.8% to 72.7% per study. The laboratory methods domain (e.g. description of testing algorithm) had the lowest median adherence (33.3% [IQR 25.0%–41.7%%]), while the discussion domain had the highest median adherence (75.0% [IQR 50.0%–100.0%])). There were no significant changes in reporting adherence to ROSES-S before and after guideline publication. Article publication source (p<0.001), study risk of bias (p=0.001), and sampling method (p=0.004) were significantly associated with adherence to the ROSES-S guideline. Conclusions The completeness of reporting in SARS-CoV-2 seroepidemiologic studies was suboptimal, especially in laboratory methods, and was associated with key study characteristics. Publication of the ROSES-S guideline was not associated with changes in reporting practices. Given that reporting is necessary to improve the standardization and utility of seroprevalence data in evidence synthesis, authors should improve adherence to the ROSES-S guideline with support from stakeholders.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,566 | 0,773 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,010 |
| Bibliométrie | 0,015 | 0,017 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».