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 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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".