The Influence of Hospitalization or Intensive Care Unit Admission on Declines in Health-Related Quality of Life
Bibliographic record
Abstract
RATIONALE: Survivors of critical illness report impaired health-related quality of life (HRQoL) after hospital discharge, but the degree to which these impairments are attributable to critical illness is unknown. OBJECTIVES: We sought to examine changes in HRQoL associated with an intensive care unit (ICU) stay and the differential association of type of hospitalization (critical illness versus noncritical illness) on changes in HRQoL. METHODS: We identified 11,243 participants in the Ambulatory Care Quality Improvement Project (a multicenter randomized trial of Veterans conducted March 1997 to August 2000) completing at least two Medical Outcomes Study Short-Form 36 questionnaires over 2 years, and categorized patients by hospitalization status during the interval between measures. We used multiple linear regression with generalized estimating equations for analysis. MEASUREMENTS AND MAIN RESULTS: Our primary outcome was change in the Physical Component Summary score. Participants requiring hospitalization or ICU admission had significantly worse baseline HRQoL than those not hospitalized (P < 0.001). Compared with patients who were not hospitalized, follow-up Physical Component Summary scores were lower among non-ICU hospitalized patients and ICU patients (adjusted β-coefficient = -1.40 [95% confidence interval, -1.81, -0.99] and adjusted β-coefficient = -1.53 [95% confidence interval, -2.11, -0.95], respectively), with no difference between the two groups (P value = 0.80). Similar results were seen for the Mental Component Summary score and each of the Medical Outcomes Study Short-Form 36 subdomains. CONCLUSIONS: Prehospital HRQoL is a significant determinant of HRQoL after hospitalization or ICU admission. Hospitalization is associated with increased risk of impairment in HRQoL after discharge, yet the overall magnitude of this reduction is small and similar between non-ICU hospitalized and critically ill patients.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".