End-Stage Renal Disease Status and Critical Illness in the Elderly
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Elderly patients (> 65 years old) are a rapidly growing demographic in the ESRD and intensive care unit (ICU) populations, yet the effect of ESRD status on critical illness in elderly patients remains unknown. Reliable estimates of prognosis would help to inform care and management of this frail and vulnerable population. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The effect of ESRD status on survival and readmission rates was examined in a retrospective cohort of 14,650 elderly patients (>65 years old) admitted to 11 ICUs in Winnipeg, Manitoba, Canada between 2000 and 2006. Logistic regression models were used to adjust odds of mortality and readmission to ICU for baseline case mix and illness severity. RESULTS: Elderly ESRD patients had twofold higher crude in-hospital mortality (22% versus 13%, P < 0.0001) and readmission rate (6.4 versus 2.7%, P = 0.001). After adjustment for illness severity alone or illness severity and case mix, the odds ratio for mortality decreased to 0.85 (95% CI: 0.57 to 1.25) and 0.82 (95% CI: 0.55 to 1.23), respectively. In contrast, ESRD status remained significantly associated with readmission to ICU after adjustment for other risk factors (OR 2.06 [95% CI: 1.32, 3.22]). CONCLUSIONS: Illness severity on admission, rather than ESRD status per se, appears to be the main driver of in-hospital mortality in elderly patients. However, ESRD status is an independent risk factor for early and late readmission, suggesting that this population might benefit from alternative strategies for ICU discharge.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".