Sunitinib, sorafenib and other systemic noncytotoxic kidney cancer therapies can and should be administered by urologists
Bibliographic record
Abstract
U rology is not just a surgical discipline.More than surgeons from other surgical disciplines, urologists have a long tradition of managing patients with multimodality therapies, including systemic therapies.The most obvious example of this is the use of androgen deprivation therapy for prostate cancer.Other examples include the use of intravesical chemotherapy for bladder cancer, and tumour vaccines and cytokines for renal cancer.Urologists have been at the forefront of research into many systemic therapies for cancer, including androgen-deprivation therapies, anti-androgens, gene therapy for prostate cancer, and atrasentan, zoledronic acid, estramustine, and gamma interferon for kidney cancer.Urologists were involved in the development of systemic chemotherapy for metastatic testicular cancer.For the first 10 years after the introduction of multi-agent chemotherapy for testicular cancer, urologic oncologists administered these drugs in many centres.These physicians acquired clinical skills in the management of patients with metastatic disease and the toxicities associated with these drugs.Why did this change?Primarily because of drug toxicity.Multi-agent, platinum-based cytotoxic chemotherapies can be lethal to patients.Methotrexate-vinblastineadriamycin-cis-platinum chemotherapy, which was standard therapy for advanced prostate cancer for 20 years, induces fatal neutropenic sepsis in 2%-4% of patients.Physicians administering these regimens need to be highly focused on the morbidities associated with these drugs and the interventions required to manage them.In this new era of noncytotoxic systemic therapies for cancer, of which the tyrosine-kinase inhibitors are excellent examples, these drugs do have associated toxicities, but their side effects are rarely life-threatening.These drugs do not induce neutropenia; they induce hypertension, handfoot syndrome and other non-life-threatening toxicities.Much like many other agents used in urology, these drugs do require care and experience to administer.Patients with metastatic cancer are not easy to manage.They experience complications from their malignancies that many urologists may find challenging to treat, for example, the management of malignant ascites, expanding liver metastases or recurrent malignant pleural effusions Debate C Co om mp pe et ti in ng g i in nt te er re es st ts s: : None declared.C Co or rr re es sp po on nd de en nc ce
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".