Letter to the Editor From Nelson et al: “Understanding the Link Between Obesity and Severe COVID-19 Outcomes: Causal Mediation by Systemic Inflammatory Response”
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.037 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".