Sex and critical illness
Bibliographic record
Abstract
PURPOSE OF REVIEW: The article reviews and speculates on potential mechanisms underlying sex-related differences in admission patterns, care delivery and outcome of critical illness. RECENT FINDINGS: Evidence from many countries suggests men are more commonly admitted to intensive care units than are women, and may be more likely to receive aggressive life support. These differences may be confounded by differences in incidence of conditions leading to critical illness, such as acute lung injury and sepsis, both more common among men, or to differences in provision of medical or surgical care that require intensive care unit. There may be different decision-making by patients or decision makers that is dependent upon age and sex of the patient and relation to the surrogate. It is unclear whether differences exist in clinical outcomes; if they do, the magnitude may be greatest among older patients. We describe potential biologic rationales and review animal models. Finally, we explore sex-based differences in the inclusion of men and women in clinical research that underlie our understanding of critical illness. SUMMARY: Sex differences in incidence of critical illness and provision of care exist but it is unclear whether they relate to differences in risk factors, or differences in decision-making among patients, surrogates or healthcare professionals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".