Identification of underserved areas for urologic cancer care
Bibliographic record
Abstract
BACKGROUND: The delivery of urologic oncology care is susceptible to regional variation. In the current study, the authors sought to define patterns of care for patients undergoing genitourinary cancer surgery to identify underserved areas for urologic cancer care in Washington State. METHODS: The authors accessed the Washington State Comprehensive Hospital Abstract Reporting System from 2003 through 2007. They identified patients undergoing radical prostatectomy, radical cystectomy (RC), partial nephrectomy (PN), radical nephrectomy, and transurethral resection of the prostate (TURP). TURP was included for comparison as a reference procedure indicative of access to urologic care. Hospital service areas (HSAs) are where the majority of local patients are hospitalized; hospital referral regions (HRR) are where most patients receive tertiary care. The authors created multivariate hierarchical logistic regression models to examine patient and HSA characteristics associated with the receipt of urologic oncology care out of the HRR for each procedure. RESULTS: Greater than one-half of patients went out of their HRR in 7 HSAs (11%) for radical prostatectomy, 3 HSAs (5%) for radical nephrectomy, 10 HSAs (15%) for PN, and 14 HSAs (22%) for RC. No HSAs had high export rates for TURP. Few patient factors were found to be associated with surgical care out of the HRR. High-export HSAs for PN and RC exhibited lower socioeconomic characteristics than low-export HSAs, adjusting for HSA population, race, and HSA procedure rates for PN and RC. CONCLUSIONS: Patients living in areas with lower socioeconomic status have a greater need to travel for complex urologic surgery. Consideration of geographic delineation in the delivery of urologic oncology care may aid in regional quality improvement initiatives.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".