Interleukin-2 in the treatment of unresectable or metastatic renal cell cancer: Time to write the obituary?
Bibliographic record
Abstract
Given the availability of new therapies to treat renal cell cancer, is there still a role for further investigation of the high-dose IL-2 approach?Probably yes, but investigations should be done in centres that have the appropriate experience and expertise.Investigation of lower intensity IL-2 has proven disappointing; studies should now focus on predictive markers to identify a subset of patients most likely to benefit.Should we attempt to develop such expertise in Canada?The principle of distributive justice would argue that we should not.In Canada, we are struggling to provide therapies that do have a proven survival benefit but also a significant incremental cost.The new targeted therapies for renal cell cancer are some of many that fall into this category.In such a setting of resource restriction, it is difficult to justify the development of a highly expensive, highly toxic therapy that benefits only a small fraction of patients with renal cell cancer.The review by Hotte and colleagues is a useful and timely requiem for IL-2 in Canada.
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.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".