Kidney Cancer Screening and Epidemiology
Bibliographic record
Abstract
The incidence of renal cell carcinoma (RCC) has risen worldwide over the past few decades, and this has been associated with a stage shift. Survival outcomes of RCC depend largely on the stage at diagnosis. Although overall mortality has stabilized or declined in most countries, survival remains poor in late-stage disease, suggesting early detection may improve overall survival outcomes. A number of potential candidate screening tools have been considered (including urinary dipstick, blood- and urine-based biomarkers, ultrasound, and computed tomography [CT]), though it may be that a combination of these approaches may be optimal. Ultimately, the sensitivity and specificity of the chosen screening tool will determine the rate of false positives and false negatives, which must be minimized. One of the key challenges is the relatively low prevalence of the disease, which might be overcome by performing risk-stratified screening or screening for more than one condition (such as combined lung and kidney cancer screening). Both approaches have been shown to be acceptable to the general public, and they may maximize the efficiency of screening while reducing harms. Indeed, quantifying benefits and harms of screening is key (including the impact on overdiagnosis and quality of life). Whether screening for RCC will lead to a stage shift and the impact on survival are the decisive missing pieces of information that will determine whether the screening program might be adopted into clinical practice (along with feasibility, acceptability, and cost-effectiveness).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".