Professional practice gaps and barriers to optimal care of renal cell carcinoma (RCC) among oncologists in the United States.
Bibliographic record
Abstract
404 Background: New therapies for advanced RCC have improved patient outcomes while increasing the complexity of care. We sought to quantify practice gaps and barriers to optimal care among oncologists treating patients with RCC at academic and/or community centers in the United States. Methods: In total, 248 oncologists were recruited for a 2-phase (qualitative/quantitative) study. Eligible participants who had fully completed either one of the 2 phases were included in the analyses (n = 169). In the first phase, participants (n = 27) completed a brief online case-based survey and a 45-minute telephone interview on the attitudinal, contextual, and behavioral factors that influenced diagnosis and treatment. Selected interviews were transcribed and analyzed through thematic analysis. In the second phase, participants (n = 142) completed an online survey including case vignettes. Respondents’ answers were compared with optimal answers based on National Comprehensive Cancer Network kidney cancer guidelines (version 1.2013) and evidence-based opinions of 2 RCC experts. Results: Forty-six percent of participants correctly identified all predictors of short survival/poor risk in RCC. Regarding treatment options for a poor-risk patient, 37.5% chose temsirolimus and 11% sunitinib, both felt to be reasonable options. In a scenario focused on dose and treatment modification in a patient with treatment-related hypertension, 34% selected a nonoptimal management option. In a scenario focused on the importance of recognizing clinical symptoms as a component of treatment decision making, 40% of respondents were in agreement with expert- and evidence-supported treatment approach. Detailed results of this analysis will be presented. Conclusions: This study revealed clinically relevant practice performance gaps that affect delivery of care and patient health outcomes. Not recognizing predictors of poor risk or the importance of evaluating clinical symptoms can result in missed opportunities to change treatment strategy, leading to suboptimal outcomes. These results will support design of educational programs and performance improvement interventions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".