Onco-nephrology: a decalogue: Table 1.
Bibliographic record
Abstract
Onco-nephrology is an evolving subspecialty that focuses on the complex relationships existing between kidney and cancer. In this opinion piece, we propose a 'decalogue of onco-nephrology', in order to highlight the areas where the nephrologist and oncologist should work closely over the ensuing years to provide cutting-edge care for patients afflicted with cancer and kidney disease. The 10 points we have highlighted include (1) acute kidney injury and chronic kidney disease in cancer patients; (2) nephrotoxic effects of anticancer therapy, either traditional chemotherapeutics or novel molecularly targeted agents; (3) paraneoplastic renal manifestations; (4) management of patients nephrectomized for a kidney cancer; (5) renal replacement therapy and active oncological treatments; (6) kidney transplantation in cancer survivors and cancer risk in ESRD patients; (7) oncological treatment in kidney transplant patients; (8) pain management in patients with cancer and kidney disease, (9) development of integrated guidelines for onco-nephrology patients and (10) clinical trials designed specifically for onco-nephrology. Following these points, a multidisciplinary onco-nephrology team will be key to providing outstanding, cutting-edge care in both the acute and chronic setting to these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".