Expert Consensus on the Management of Advanced Clear-cell Renal Cell Carcinoma: Indian Perspective (Pearl-india)
Bibliographic record
Abstract
Abstract In advanced Renal Cell Carcinoma (aRCC), systemic therapy is the mainstay of treatment, with no or little role for surgery in these patients. Tyrosine kinase inhibitors (TKIs) and immune-oncological (IOs) therapies, either alone or in combination, are recommended in these patients depending on patient and tumour factors. The sequencing of therapies is critical in RCC because the choice of subsequent line therapy is heavily dependent on the response and duration of the previous treatment. There are additional barriers to RCC treatment in India. Immunotherapy is the cornerstone of treatment in ccRCC, but it is prohibitively expensive and not always reimbursed, effectively putting it out of reach for the vast majority of eligible patients in India. Furthermore, in advanced RCC (particularly the clear cell variety), Indian oncologists consider the disease burden of the patients, which is particularly dependent on the quantum of the disease load, clinical symptoms, and performance status of the patient, before deciding on treatment. There are no India-specific guidelines for clear cell RCC (ccRCC) treatment or the positioning and sequencing of molecules in the management of advanced ccRCC that take these country-specific issues into account. The current consensus article provides expert recommendations and treatment algorithms based on existing clinical evidence, which will be useful to specialists managing advanced ccRCC.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".