Practice challenges affecting optimal care as identified by US medical oncologists who treat renal cell carcinomas
Bibliographic record
Abstract
BACKGROUND: Approval of new agents provides alternative treatment options for medical oncologists and their patients with renal cell carcinoma (RCC). Treatment decisions remain challenging in the absence of clear evidence supporting optimal selection and sequencing of treatment for different patient or tumor characteristics. OBJECTIVE: To assess the clinical practice gaps of medical oncologists treating patients with RCC. METHODS: Medical oncologists practicing in the United States with a case load of 1 or more RCC patient(s) a year were recruited to participate in either an online case-based survey followed by a 45-minute interview (phase 1) or a 15-minute online survey with case vignettes (phase 2). Respondents' answers were compared with treatment guidelines and faculty experts' recommendations. RESULTS: Qualitative interviews (n = 27) and quantitative surveys (n = 142) were compiled. Clinical performance gaps demonstrating oncologists' diffculties to optimally adjust their treatment plan were identifed. When presented with an RCC patient with treatment-related hypertension, 34% of respondents did not select an expert-recommended option. In a scenario focused on recognizing clinical signs and symptoms as an important component of treatment decision-making, 40% of respondents agreed with the expert-recommended approach. For a progressive patient with chronic obstructive pulmonary disease, 78% of respondents were misaligned with evidence-based treatment options. LIMITATIONS: Self-selection and respondent bias may have occurred. Sample size may have limited the statistical power. CONCLUSIONS: This study identifed clinically relevant performance gaps among US oncologists treating RCC patients. Education to assure familiarity with the most recent changes is needed. FUNDING/SPONSORSHIP: Pfzer Medical Education Group provided fnancial support through an educational research grant.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".