Examining the management of muscle-invasive bladder cancer by medical oncologists in the United States
Bibliographic record
Abstract
BACKGROUND: Neoadjuvant chemotherapy (NACT) for the treatment of muscle-invasive bladder cancer (MIBC) remains underutilized in the United States despite evidence supporting its use. OBJECTIVES: To examine the perioperative chemotherapy management of patients with MIBC by medical oncologists (MedOncs) to move toward standardization of practice PARTICIPANTS AND METHODS: A 26-question survey was emailed to 92 MedOncs belonging to the Bladder Cancer Advocacy Network or the American Society of Clinical Oncology for completion from May to October 2011 RESULTS: A total of 83 MedOncs completed the survey: 52% were based in academic centers. Most referrals were from urologists (79%). NACT for treatment of MIBC and high-grade upper-tract urothelial carcinoma is offered by 80% and 46% of respondents, respectively. Adjuvant chemotherapy for treatment of MIBC and upper-tract urothelial carcinoma is offered by 46% and 42% of respondents, respectively. NACT was not offered by 49%, 29%, and 35% of respondents if Eastern Cooperative Oncology Group performance status was 3 or greater, if patients had T2 lesions without lymphovascular invasion, and if the glomerular filtration rate was<50ml/min, respectively. Chemotherapy regimens included gemcitabine/cisplatin (90%), methotrexate/vinblastine/adriamycin/cisplatin (30%), dose-dense methotrexate, vinblastine, adriamycin, and cisplatin (20%), and gemcitabine/carboplatin (37%). CONCLUSIONS: Most MedOncs (79%) in this survey offer perioperative chemotherapy to all patients with MIBC. This increased use of NACT is higher than previously reported, suggesting an increase in the adoption of recommendations that follow best evidence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".