Mixed methods assessment of providers’ needs in the management of advanced renal cell carcinoma.
Bibliographic record
Abstract
313 Background: There is a paucity of data regarding the challenges associated with the timely and accurate diagnosis of Renal Cell Carcinoma (RCC) and its effective multidisciplinary management. This study aimed to assess the knowledge and skills of healthcare providers (HCPs) managing and coordinating the care of patients with advanced RCC (aRCC) across multidisciplinary teams. Methods: A sequential mixed methods needs assessment was conducted across the United States with medical oncologists (ONCs), nephrologists (NEPHs), physician assistants (PAs), nurse practitioners (NPs), and registered nurses (RNs). Interviews, transcribed and thematically analyzed, and online surveys, statistically analyzed, were triangulated. Results: A total of 305 HCPs completed an interview (n=40) or the survey (n=265): 78 ONCs, 62 NEPHs, 57 PAs, 55 NPs, 53 RNs. One third (33%) of HCPs reported suboptimal skills in adjusting the dose of a treatment for aRCC in the event of adverse reactions. Interviews underscored a lack of clarity for HCPs on when to reduce the dose or when to discontinue and/or switch to other drugs. Suboptimal knowledge and skills related to toxicities were found (Table). Breakdowns in communication across multidisciplinary teams were identified by 46% of HCPs. Of those, 61% occurred when monitoring side effects and 48% when referring to ONCs. Some NEPHs reported never, rarely, or sometimes being involved with ONCs in the management of nephritis (25%), chronic kidney disease (19%), or acute renal failure (24%). Interviews suggested the role of NEPHs in the care of aRCC is poorly recognized and that NEPHs are perceived to have limited time to spend in the care of cancer patients. Few NEPHs reported gaps in knowledge/skills managing key renal complications such as nephritis (13%/15%), chronic kidney disease (6%/15%), and acute renal failure (9%/9%). Conclusions: This study identified a need to improve HCPs’ knowledge of the signs/symptoms of treatment side effects, skills in identifying/referring patients to appropriate specialists, and skills in managing adverse events. Barriers to involving NEPHs in the co-management of aRCC included a lack of recognition of their role in managing treatment-induced renal complications.These results should inform educational interventions for professionals caring for aRCC patients.[Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".