A Validation Study of the Canadian Organ Replacement Register
Bibliographic record
Abstract
Background and objectives Accurate and complete documentation of patient characteristics and comorbidi-ties in renal registers is essential to control bias in the comparison of outcomes across groups of patients or dialysis facilities. The objectives of this study were to assess the quality of data collected in the Canadian Organ Replacement Register (CORR) compared with the patient’s medical charts. Design, setting, participants, & measurements This cohort study of a representative sample of adult, incident patients registered in CORR in 2005 to 2006 examined the prevalence, sensitivity, specificity, positive and negative predictive values, and of comorbid conditions and agreement in coding of patient demographics and primary renal disease between CORR and the patient’s medical record. The effect of coding variation on patient survival was evaluated. Results Medical records on 1125 patients were reviewed. Agreement exceeded 97 % for health card number, date of birth, and sex and 71 % (range 46.6 to 89.1%) for the primary renal disease. Comorbid conditions were under-reported in CORR. Sensitivities ranged from 0.89 (95 % confidence interval 0.80, 0.92) for hyper-tension to 0.47 (0.38, 0.55) for peripheral vascular disease. Specificity was 0.93 for all comorbidities except hypertension. Hazard ratios for death were similar whether calculated using data from CORR or the medi-cal record. Conclusions Comorbid conditions are under-reported in CORR; however, the associated risks of mortality were similar whether using the CORR data or the medical record data, suggesting that CORR data can be used in clinical research with minimal concern for bias.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".