Quality of Life Before Intensive Care Using EQ-5D
Bibliographic record
Abstract
OBJECTIVE: To compare patients' retrospectively reported baseline quality of life before intensive care hospitalization with population norms and proxy reports. DESIGN: Prospective cohort study. SETTING: Thirteen ICUs at four teaching hospitals in Baltimore, MD. PATIENTS: One hundred forty acute lung injury survivors and their designated proxies. INTERVENTIONS: Around the time of hospital discharge, both patients and proxies were asked to retrospectively estimate patients' baseline quality of life before hospital admission using the EQ-5D quality-of-life instrument. MEASUREMENTS AND MAIN RESULTS: Mean patient-rated EQ-5D visual analog scale scores and utility scores were significantly lower than population norms but were significantly higher than proxy ratings. However, the magnitude of difference in average utility scores between patients and either population norms or proxies was not clinically important. For the five individual EQ-5D domains, κ statistics revealed slight to fair agreement between patients and proxies. Bland-Altman plots demonstrated that for both the visual analog scale and utility scores, proxies underestimated scores when patients reported high ratings and overestimated scores for low patient ratings. CONCLUSIONS: Patients retrospectively reported worse baseline health status before acute lung injury than population norms and better status than proxy reports; however, the magnitude of these differences in health status may not be clinically important. Proxies had only slight to fair agreement with patients in all five EQ-5D domains, attenuating patients' more extreme ratings toward moderate scores. Caution is required when interpreting proxy retrospective reports of baseline health status for survivors of acute lung injury.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".