Sociodemographic disparities in the treatment of small renal masses
Bibliographic record
Abstract
OBJECTIVE: To examine the presence of specific sociodemographic disparities in the treatment of individuals with small renal masses. PATIENTS AND METHODS: Patients diagnosed with pT1aN0M0 renal cell carcinoma (RCC) were identified from the Surveillance, Epidemiology, and End Results database (years 1988-2008). Treatment type was stratified into non-surgical and surgical management and the group of patients who underwent surgical intervention was further stratified into those who underwent partial nephrectomy (PN) and those who underwent radical nephrectomy (RN). The main variables of interest were race and gender, as well as family income and poverty and education levels. Temporal trend analyses and logistic regression models were performed. RESULTS: Of 26,468 patients with T1aN0M0 RCC, 2797 (10.6%) were non-surgically managed and 23,671 (89.4%) underwent surgery. Of the latter, 14,705 (62.1%) underwent RN and 8966 (37.9%) PN. In multivariable analyses, black patients were 23% more likely to be non-surgically managed than other ethnic groups, and if surgically managed, were 20% less likely to undergo PN (both P ≤ 0.007). Men were 19% more likely than women to be non-surgically managed, but remained 14% more likely to receive a PN (both P < 0.001). Treatment disparities according to income, education and poverty level were recorded. Poverty (odds ratio [OR]: 1.002) and education (OR: 0.998) proxies emerged as important determinants of non-surgical management, whereas income (OR: 1.08, all P ≤ 0.02) was a determinant of PN. CONCLUSIONS: Social inequalities regarding access to treatment remain prevalent among patients diagnosed with small renal masses. The persistence of such a phenomenon is a concerning trend which merits further investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".