Prevalence of Anemia in the Nursing Home: Contribution of Chronic Kidney Disease
Bibliographic record
Abstract
OBJECTIVES: To assess the independent contribution of chronic kidney disease (CKD) and age to anemia in older nursing home residents. DESIGN: Retrospective. SETTING: Skilled nursing facility. PARTICIPANTS: Nursing home residents with records in the Beverly Healthcare Data Warehouse who were admitted to a nursing home between January 1, 2002, and December 31, 2003; were alive as of January 31, 2004; and had hemoglobin and serum creatinine (SCr) values available for analysis. MEASUREMENTS: Prevalence of anemia (hemoglobin <13 g/dL for men and <12 g/dL for women) and CKD (estimated glomerular filtration rate <60 mL/min per 1.73 m(2), according to Modification of Diet in Renal Disease criteria) and the contribution of CKD and age to the prevalence of anemia. RESULTS: Six thousand two hundred resident records were analyzed (70% female, 85% Caucasian). Overall, 59.6% of residents were anemic, and 43.1% had CKD, and residents with CKD were more likely to have anemia (64.9% with vs 55.7% without CKD; odds ratio (OR)=1.47, 95% confidence interval (CI)=1.33-1.63). Although older age was associated with lower hemoglobin values primarily in residents without CKD (Spearman rank correlation coefficient (r)=-0.10, P<.001), age had no association with hemoglobin in CKD (Spearman r=0.01, P=.60). The greater risk of anemia in the presence of CKD persisted in each age category (OR=2.07, 95% CI=1.53-2.80, aged 65-74; OR=1.44, 95% CI=1.21-1.70, aged 75-84; and OR=1.35, 95% CI=1.15-1.57, aged > or =85). CONCLUSION: Overall, these results suggest that CKD contributes more strongly than older age to the high prevalence of anemia in older nursing home residents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".