Bibliographic record
Abstract
Incidentally detected, small renal masses (SRMs) have been increasing significantly in recent years due to the widespread use of improved cross-sectional imaging. A significant number of incidental SRMs are diagnosed in elderly patients who are more likely to undergo imaging for other medical issues. The natural history of SRMs has not been historically well understood because most masses are surgically removed soon after diagnosis. Several reports of surveillance of SRMs have been published in the last few years. When followed conservatively with serial imaging, SRMs have variable growth rates with an average of 0.28 cm/year, according to a recent meta-analysis. Larger series with longer follow-up are needed, but a significant number of small tumors seem to have an indolent behavior with a slow growth rate and a limited tendency to progress. The standard of care for enhancing SRMs is surgery. Up to one-third of surgically removed, <4-cm tumors are histologically benign. The outcomes of current surgical treatment of histologically confirmed, <4-cm, renal cell carcinomas are excellent, but this has not led to a decrease in mortality. Based on these considerations and on the available data on the natural history of SRMs, it seems reasonable to consider that we may be overtreating these lesions. This is especially true for elderly or unfit patients who have a decreased life expectancy. In these selected patients and in patients who refuse active treatment, it seems reasonable to propose an initial period of active surveillance for incidental SRMs, with delayed intervention for those tumors that will exhibit fast growth during follow-up. Percutaneous needle biopsies of renal tumors can be safely performed with the use of modern techniques and have the potential to characterize SRMs at histologically diagnosis, thereby allowing a better selection of the conservative or active treatment that is best suited for each individual patient.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".