Predictors of suboptimal and crash initiation of dialysis at two tertiary care centers
Bibliographic record
Abstract
Many end-stage renal disease patients do not have an optimal start to dialysis. Many patients have suboptimal initiation, while others "crash" start on dialysis without prior care from a nephrologist. We examined factors associated with suboptimal or crash starts. We conducted a retrospective cohort study of 377 incident dialysis patients at two tertiary care centers from January 2006 to April 2011. Logistic regression was used to identify factors associated with suboptimal and crash starts to dialysis. Out of 377 patients, 102 (27%) had optimal starts, 221 (59%) had suboptimal starts, and 54 (14%) had crash starts. Three hundred thirty-four patients (89%) began with hemodialysis, while 11% started with peritoneal dialysis. Factors independently associated with a suboptimal start as opposed to an optimal start included nephrology care more than 12 months prior to initiation of dialysis (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.12-0.58), Charlson Comorbidity Index (OR, 1.25 per 1 point; 95% CI, 1.09-1.43), and age (OR, 1.02 per 1 year; 95% CI, 1.00-1.04). In comparison, diabetic nephropathy (OR, 0.25; 95% CI, 0.12-0.54), a history of pulmonary edema within 6 months prior to initiation of dialysis (OR, 3.70; 95% CI, 1.77-7.75), and a diagnosis of chronic obstructive lung disease (OR, 0.07; 95% CI, 0.01-0.52) were independently associated with a crash start. There was a low incidence of optimal dialysis starts in our tertiary care dialysis population. Our study highlights that suboptimal and crash start patients are distinct populations. Modifying factors that predict nonoptimal dialysis starts will need to consider these distinctions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".