WHO CRASHES ONTO DIALYSIS? HEALTH DETERMINANTS OF PATIENTS WHO ARE LATE REFERRED TO CHRONIC RENAL CARE IN CANADA
Bibliographic record
Abstract
Late nephrology referral, a problem currently identified across many high income countries, has been associated with reduced opportunities for delaying or halting the progression of chronic kidney disease (CKD), delayed dialysis initiation, reduced choice in treatment modality, increased morbidity and hospitalization, and premature death. Despite a recent finding that the progression of CKD nearly always presents warning signs, and despite the fact that all Canadians are entitled to receive medically necessary health care free at the point of patient entry, each year in the province of British Columbia (BC) a substantial number of people with CKD experience late or no referral to nephrology care prior to requiring renal replacement therapy. A subset of these CKD patients experience no referral and “crash” onto dialysis (experience an acute or emergent start). Existing research has not fully explored the range of potential health determinants that may affect the timing of nephrology referral. This paper adopts a “determinants of health” framework and assesses the impact of a variety of indicators on patients’ physical health, demographics, socioeconomic status, social support, geographic and health system characteristics. Using a late referral definition of <3 months and data on BC patients who began dialysis between April 2000 and March 2003, multiple regression analysis indicates that the following determinants have an independent effect on the timing of referral: cause of end-stage renal disease (p=<0.0001); age (p=<0.0001); race/ethnicity (p=0.0019); English ability (p=0.0158); marital status (p=0.0202); proximity to care (p=0.0118); and, “age by first language” (p=0.0244).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".