Cognitive Impairment in Advanced CKD: The Canadian Frailty Observation and Interventions Trial (CanFIT)
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a major public health problem with increasing incidence and prevalence in North America and worldwide. CKD disproportionately affects the elderly, and leads to higher risks of kidney failure, cardiovascular events and all cause mortality. In this population, CKD is also associated with additional comorbid conditions, and an increased prevalence of frailty and disability. Frailty is a multidimensional syndrome characterized by loss of lean body mass (sarcopenia), weakness, and decreased endurance, leading to reduced activity and a poor response to stressors. Several prospective studies in the general population have shown that measures of frailty are strongly associated with death and hospitalization in older individuals, and moreover that this association is independent of other clinical risk factors and comorbid conditions. We believe that frailty is highly prevalent in patients with CKD, and is associated with treatment decisions and outcomes. In order to explore this hypothesis, we are presently conducting a prospective cohort study examining frailty in individuals with advanced CKD. Our study will enroll 600 patients with CKD Stages 4-5, and perform tests of physical function and cognition. We will then associate frailty and its components (physical function, cognition, depression) with treatment preferences and adverse outcomes in the CKD population. The cross sectional component of this study will be completed by July 2013, and the longitudinal analysis can begin in June 2014.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".