Original Article Survival and dialysis initiation: comparing British Columbia and Scotland registries
Bibliographic record
Abstract
Background. Outcomes are a major metric for evaluating effectiveness of dialysis. Comparisons between different populations reveal significant variation. In addition, the question of optimal timing of dialysis start lacks robust data from which to generate conclusions. Methods. This study compares dialysis survival in two ge-ographically similar areas, Scotland and British Columbia, Canada (BC). The effect of eGFR at dialysis start on sur-vival was alsomeasured. Incident adult dialysis populations of Scotland (n = 3372) and BC (n = 3927), 2000–05 were compared. Mortality Hazard ratios (HR) were calculated using a Cox proportional hazards model. Multivariate anal-ysis included pre-dialysis eGFR, registry, age, sex, dialysis modality, year of start, pre-dialysis haemoglobin and pri-mary renal diagnosis. Results. Median survival times from start of dialysis were 38 (35–40) and 44 (42–47) months in Scotland and BC, re-spectively, giving an unadjusted mortality HR, Scotland versus BC, of 1.20 (95 % C.I. 1.12–1.29). BC patients started dialysis at a higher eGFR (8.9 ml/min/1.73 m2) than Scotland (7.5 ml/min/1.73 m2), and prior to modelling higher starting eGFR was associated with higher mortal-ity (1 ml/min/1.73 m2 increase, HR = 1.028; 95 % C.I. 1.021–1.035). BC patients were also older and had more diabetic renal disease. In multivariate analysis, lower start-ing eGFR was associated with better survival, and Scotland had greater mortality than BC. General population mortal-ity and transplantation rate had only minor influence. Conclusions.Concepts of ‘late ’ versus ‘early ’ start dialysis based on eGFR alone may need modification given the complexity and confounding reasons for dialysis initiation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".