Ten tips for an onco-nephrology clinic
Bibliographic record
Abstract
An increasing number of cancer patients are benefiting from long term cancer-directed therapy with an ever-expanding arsenal of novel agents from monoclonal antibodies to small molecules and cellular therapies in addition to the mainstay cytotoxic chemotherapy. All these therapies are accompanied by a unique array of adverse events, which include kidney toxicity. In this context, the need for onco-nephrology expertise continues to grow. Oncologists and hematologists collaborate closely with onco-nephrologists to determine: (i) treatment options for special populations based on accurate assessment of patients' glomerular filtration rate, (ii) supportive therapies for those whose treatment course is complicated by acute kidney injury, and (iii) pharmacologic strategies to continue or restart cancer therapy during kidney dysfunction. Here we outline 10 tips for common clinical scenarios in an outpatient onco-nephrology clinic.
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How this classification was reachedexpand
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".