The Relationship between Anemia and Chronic Kidney Disease (CKD) in Older Nursing Facility (NF) Residents.
Bibliographic record
Abstract
Abstract Anemia is a common condition among older NF residents that often has unidentified causes. CKD is common in older adults and frequently contributes to anemia. However, the association between declining kidney function and the prevalence and severity of anemia in NF residents has not been well characterized. We retrospectively analyzed the independent association of CKD and anemia among older residents in 372 NFs across the US. Any resident who was admitted to a NF between 01 Jan 2002 and 31 Dec 2003, was still active as of 31 Jan 2004, was ≥ 65 years of age, and had more than one serum creatinine (SCr) measurement and more than one hemoglobin (Hb) documented during the admittance period, was considered. Residents who had end-stage disease, were comatose, or were receiving dialysis were excluded. CKD was conservatively defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 (MDRD equation). Anemia was defined using the WHO criteria for anemia (< 13 g/dL for men and < 12 g/dL for women). Regression analysis was used to determine the association between kidney function and Hb levels after adjusting for age, race, and gender. Of the 10315 residents identified, 6200 were eligible for this study: 85% (5291/6200) were white, 70% (4354/6200) were female, and the mean ± SD age was 83.1 ± 7.9 years. Anemia was found in 59.6% (3697/6200) residents as defined by the WHO criteria and CKD was present in 43.1% (2671/6200) residents. The odds of having anemia was 1.47 times greater (95% CI: 1.33, 1.63) for residents with CKD than those without CKD. The severity of anemia also increased with the presence and severity of CKD. The odds ratio increased to 1.65 (95% CI: 1.49, 1.84) after adjusting for age, race, and gender. Of those with CKD, 36.4% (972/2671) had a Hb < 11 g/dL, however, only 5.1% (50/972) and 25.4% (247/972) received erythropoiesis-stimulating protein (ESP) and iron therapies, respectively. Our data suggest CKD contributes to the high prevalence of anemia in older NF residents. These results reinforce the importance of evaluating kidney function and Hb level in older NF residents. Future studies evaluating the impact of anemia and benefits of anemia therapy are warranted in this setting.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".