Renal tumors and the risk of malignancy based on size.
Bibliographic record
Abstract
PURPOSE: To determine the incidence of malignancy in resected renal tumors in a subpopulation of Canadian patients and the significance of tumor size, patient's demographics, and whether the tumor was an incidental finding. METHODS: Medical records of 168 consecutive nephrectomies performed between March 2003 and June 2008 at our institution were reviewed retrospectively. RESULTS: Average age of the patients was 61 years old (SD 11, range 28-89) and male to female ratio was 1.3:1. Total of 180 masses were resected in 168 nephrectomies (128 radical, 40 partial) during the study period. Of the 180 masses, 20 (11%) were benign and 160 (89%) were malignant lesions. Fifty-five percent of the resected renal masses were incidentally found on preoperative imaging. Based on the pathology reports, the average size of the masses was 5.5 cm (SD 4.0, range 0.3-25.0). The larger masses were more likely to be malignant than the smaller masses (Pearson's chi-square test, p = 0.040). CONCLUSION: The present study assists us to adequately assess the risk of malignancy of a renal mass in a Canadian population based on size which allows us to properly advise the patients and suggest best possible treatment options. We recommend more aggressive therapies for masses larger than 4 cm and parenchymal sparing procedures for masses smaller than 4 cm as large proportion of these are benign.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".