Predicting mortality after kidney transplantation: a clinical tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it