Letter to the Editor From Nelson et al: “Understanding the Link Between Obesity and Severe COVID-19 Outcomes: Causal Mediation by Systemic Inflammatory Response”
Notice bibliographique
Résumé
We have read and appreciate the hypothesis and excellent data collected and analyzed to show that inflammation related to obesity is associated with increased severity of COVID-19 illness (1). The COVID-19 pandemic has illustrated and exacerbated existing inequalities in health and well-being. At the intersection of many of the pandemic drivers, sex plays an essential and frequently overlooked role. There are several reasons why the analyses, as presented, warrant an in-depth analysis of sex. First, the published data indicate that obese patients were more likely to be female (page e701) (1). Which is sufficient reason, alone, to analyze outcomes separately by sex. Second, rates of morbidity and mortality from COVID-19, as defined by the need for assisted ventilation, longer hospitalizations, and intensive care, appear to be greater in men than in women (2, 3) despite equivalent incidence of SARS-CoV-2 disease by sex. Third, the C-reactive protein variable used to identify inflammation is reported to have a different mean population level in men vs women as documented by many years of National Health and Nutrition Examination Survey data (4). The concept that data from women and men (female and male animals, and now female and male cells) be disaggregated and that outcomes be stratified by sex is now longstanding and supported by multiple scientific institutions (5). The Endocrine Society, importantly, has specific sex-considerations in its published publication policy (https://academic.oup.com/jcem/pages/author_guidelines#OriginalArticles). The “Reporting of the Sex of Research Subjects” guidelines specify that “the sex of research subjects must be indicated. If both males and females were included in the study, the numbers of subjects from each sex should be indicated, and it must be indicated whether sex was considered a factor in the statistical analysis of data.” These authors did clearly show how many women and men they studied. But, they have not indicated whether sex was considered in the statistical analysis, or even if they adjusted for sex in the analysis. Furthermore, we note that the current JCEM policy does not meet guidelines that require separate statistical analysis by sex as do the international Sex and Gender Equity in Research (SAGER) guidelines (5, 6). Science needs to follow scientific principles and guidelines, especially when not following them introduces sex and gender bias. Women and men differ. Both sexes deserve accurate representation in research and scientific publications. All authors have read, made suggested changes to this submission, and are qualified as authors. None of the authors have a conflict of interest related to the issues involved in the original publication nor related to reporting separately by sex in scientific publications.
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 | 0,004 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,037 | 0,035 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,005 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».