Updated comorbidity assessments and outcomes in prevalent hemodialysis patients
Bibliographic record
Abstract
When evaluating clinical characteristics and outcomes in patients on hemodialysis, the prevalence and severity of comorbidity may change over time. Knowing whether updated assessments of comorbidity enhance predictive power will assist the design of future studies. We conducted a secondary data analysis of 1846 prevalent hemodialysis patients from 15 US clinical centers enrolled in the HEMO study. Our primary explanatory variable was the Index of Coexistent Diseases score, which aggregates comorbidities, as a time-constant and time-varying covariate. Our outcomes of interest were all-cause mortality, time to first hospitalization, and total hospitalizations. We used Cox proportional hazards regression. Accounting for an updated comorbidity assessment over time yielded a more robust association with mortality than accounting for baseline comorbidity alone. The variation explained by time-varying comorbidity assessments on time to death was greater than age, baseline serum albumin, diabetes, or any other covariates. There was a less pronounced advantage of updated comorbidity assessments on determining time to hospitalization. Updated assessments of comorbidity significantly strengthen the ability to predict death in patients on hemodialysis. Future studies in dialysis should invest the necessary resources to include repeated assessments of comorbidity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".