Predicting mortality after kidney transplantation: a clinical tool
Bibliographic record
Abstract
An increasing number of patients referred for transplantation are older and have complex comorbidity affecting outcome. Patient counseling is often empiric and time consuming. For the physician there are few clinical tools available to help quantify survival chances after transplantation. We used registry data to develop a series of tables that could be used in the clinical setting to predict survival probability. Using data from the Canadian Organ Replacement Registry, we generated clinical survival tables using Cox's regression model. Model covariates included age, race, gender, treatment period, primary renal disease cause, donor source, months on dialysis and comorbidities. A total of 6324 patients were included, 22% had > or =1 comorbid condition at baseline. After adjustment for age, gender and cause of renal disease, increased comorbidity was strongly associated with reduced patient-survival (P < 0.05). Age and comorbidity specific clinical survival tables showing the expected 1-, 3- and 5-year patient survival probabilities were generated. Separate tables were created for diabetics, nondiabetics, living-donor organs and deceased-donor transplantation. Patient-specific survival data can be estimated from registry data. We suggest annual or biannual tables generated by national registries across Europe and N. America, may be useful to those physicians faced with counseling patients and families.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".