Canadian Organ Replacement Register (CORR): Reflecting the Past and Embracing the Future
Bibliographic record
Abstract
INTRODUCTION: The Canadian Organ Replacement Register (CORR) is the only Canadian information system on kidney and extra-kidney organ failure and transplantation in Canada. CORR's mandate is to record and analyze the level of activity and outcomes of vital organ transplantation and treatment of end stage kidney disease using dialysis, either hemodialysis or peritoneal dialysis, activities across Canada. The Canadian Organ Replacement Register was officially launched in 1987, and it included transplantation of extra-renal vital organs (liver, heart, lung, pancreas, bowel), in addition to renal transplantation and replacement therapy, with new financial support from the provinces. OBJECTIVE: This manuscript describes the process of data acquisition and reporting, focusing on the patients with end stage kidney disease on dialysis, with data reported from the 2014 CORR Annual Data Report and the Center-Specific Reports on Clinical Measures. METHODS: CORR is currently housed in the Canadian Institute for Health Information and collects data from hospital dialysis programs, regional transplant programs, organ procurement organizations and kidney dialysis services offered at independent health facilities. Data on patients is collected by completion of survey forms for each patient at the start of dialysis or receiving a transplant, using the Initial Registration form, and yearly follow up forms, which collects data on the status of the patient as of October 31(st). RESULTS: The incident rate per million population (RPMP) has remained stable with the exception of the 65+ age group with has experience a modest decrease since 2001. However, there has been an increasing prevalence of ESKD diagnoses, with the highest rate per million population (RPMP) amongst the age group 65+ years. This is likely attributed to gradual improving patient survival. Between 2003 and 2012, nearly 90% of dialysis patients younger than <18 and 26% of patients 75+ years survived for at least five years. CONCLUSION: As the number of people treated for end-stage organ failure grows, so does the importance of understanding their treatment and outcomes. In 2014, CORR continues to evolve and support the important information need to advance ESRD research and clinical practice.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".