Cancer Management During the COVID-19 Pandemic in the United States
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 (COVID-19) has significantly impacted health care delivery across the United States, including treatment of cancer. We aim to describe the determinants of treatment plan changes from the perspective of oncology physicians across the United States during the COVID-19 pandemic. METHODS: Participants were recruited to an anonymous cross-sectional online survey of oncology physicians (surgeons, medical oncologists, and radiation oncologists) using social media from March 27 to April 10, 2020. Physician demographics, practice characteristics, and cancer treatment decisions were collected. RESULTS: The analytic cohort included 411 physicians: 241 (58.6%) surgeons, 106 (25.8%) medical oncologists, and 64 (15.6%) radiation oncologists. In all, 38.0% were practicing in states with 1001 to 5000 confirmed COVID-19 cases as of April 3, 2020, and 37.2% were in states with >5000 cases. Most physicians (N=285; 70.0% of surgeons, 64.4% of medical oncologists, and 73.4% of radiation oncologists) had altered cancer treatment plans. Most respondents were concerned about their patients' COVID-19 exposure risks, but this was the primary driver for treatment alterations only for medical oncologists. For surgeons, the primary driver for treatment alterations was conservation of personal protective equipment, institutional mandates, and external society recommendations. Radiation oncologists were primarily driven by operational changes such as visitor restrictions. CONCLUSIONS: The COVID-19 pandemic has caused a majority of oncologists to alter their treatment plans, but the primary motivators for changes differed by oncologic specialty. This has implications for reinstitution of standard cancer treatment, which may occur at differing time points by treatment modality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".