Sex-specific prevalence and outcomes of frailty in critically ill patients
Bibliographic record
Abstract
BACKGROUND: The prevalence of frailty, an important risk factor for short- and long-term outcomes in hospitalized adults, differs by sex. Studies in critically ill adults have also found differences in mortality and organ support rates in males and females. The objective of this study was to determine if these observed differences in mortality and organ support rates can be explained by sex and frailty alone, or if the interaction between sex and frailty is an important risk factor. METHODS: This is a retrospective multi-centre population-based cohort study of all adult patients (≥ 18 years) admitted to the seventeen intensive care units (ICUs) across Alberta, Canada, between 2016 and 2017. On admission, physicians assigned a Clinical Frailty Scale (CFS) score (1 = very fit, 9 = terminally ill) to all patients. Patients with missing CFS scores or who died within 24 h of ICU admission were excluded. Frailty was defined as CFS ≥ 5. Outcomes included all-cause hospital mortality, ICU mortality, and organ support rates. A propensity score for female sex was generated and 1:1 matching on sex was performed. Multivariable Cox regression or logistic regression, as appropriate, was performed to evaluate the association between sex, frailty, and the sex-frailty interaction term with outcomes. RESULTS: Of 15,238 patients included in the cohort, after propensity score matching 11,816 patients remained (mean [standard deviation] age 57.3 [16.9]). In the matched cohort, females had a higher prevalence of frailty than males (32% vs. 27%, respectively) and higher odds of frailty (odds ratio [95% confidence interval (CI)] 1.29 [1.20-1.40]). Though females were less likely to receive invasive mechanical ventilation (hazard ratio [95% CI] 0.78 [0.71-0.86]), the interaction between sex and frailty (i.e., males and females with and without frailty) was not associated with differences in organ support rates. Receipt of dialysis and vasoactive support, as well as hospital mortality and ICU mortality were associated with frailty but were not associated with female sex or the interaction between sex and frailty. CONCLUSIONS: Although frailty and sex were individually associated with mortality and differences in organ support in the ICU, there does not appear to be a significant interaction between sex and frailty with regards to these outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".