Sex‐dependent disparities in critical illness: methodological implications for critical care research
Bibliographic record
Abstract
Despite persistent efforts by government and research funding agencies to include sex and gender in human health-related research (Institute of Medicine, 2001) and acknowledgement that sex and gender are major determinants of many health-related outcomes (Vlassof, 2007), exploration of these issues in critical illness remains sparse. Historically, sex differences referred to biological dissimilarities between males and females, whereas, gender differences suggested the effect of psychosocially conditioned factors and sociopolitical environments (Holdcroft, 2007). More recently, epigenetic studies have clearly demonstrated interactions between social determinants (like gender) and past experiences and cellular responses to stress (Miller et al., 2011; Stankiewicz et al., 2013; Babenko et al., 2014), suggesting a blurring of these independent definitions. We will use the more inclusive term gender in this review, unless summarizing what authors have stated are sex-specific physiological processes.Although the gender gap in life expectancy, favouring women by 5 years on average (Rochelle et al., 2014), is still unexplained, women exhibit specific health-related susceptibilities (e.g. autoimmune, mood and anxiety disorders) compared to men (Verma et al., 2011). Moreover, in such disease states as coronary artery disease, health-related outcomes are less favourable in women. While some differences are likely attributed to differences in treatments and insidious gender bias (Alspach, 2012), in some conditions women may still have worse outcomes, despite identical care (Anderson and Pepine, 2007).In spite of a long-held belief that women have a better chance to survive critical illness (Kristensen et al., 2014), a closer look at the evidence reveals a complex interactive picture. This editorial aims to briefly synthesize current evidence on the influence of sex and gender on adult critically ill patients' outcomes and to discuss specific methodological implications for critical care research.
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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 | 0.157 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".