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Record W2049101310 · doi:10.1097/mcc.0b013e3283307a12

Sex and critical illness

2009· review· en· W2049101310 on OpenAlexaff
Robert Fowler, Woganee A Filate, Michael Hartleib, David Frost, Chris Lazongas, Michelle Hladunewich

Bibliographic record

VenueCurrent Opinion in Critical Care · 2009
Typereview
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCritical illnessMEDLINEIntensive care medicineCritically ill

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.417
GPT teacher head0.571
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations33
Published2009
Admission routes1
Has abstractyes

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