Virtual Care for Patients with Advanced Well Differentiated Gastroenteropancreatic Neuroendocrine Tumor (GEP-NET)
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic resulted in an unprecedent shift towards virtual cancer care, including the care of gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The aim of this study was to evaluate the use of virtual care for GEP-NETs during the COVID-19 pandemic at a high-volume academic cancer center. METHODS: This retrospective, observational study performed at the Ottawa Hospital Cancer Center in Canada evaluated adult patients with GEP-NETs seen in consultation by medical oncology between 1 June 2019 and 31 December 2022. Demographic, clinicopathologic, cancer treatment and visit data were collected. Univariable and multivariable analyses assessed the relationship between patient characteristics and virtual care use. RESULTS: A total of 103 patients with well-differentiated GEP-NETS were included. Overall, 18/103 (17.5%) consults and 594/781 (76.1%) follow-ups were performed virtually. All consultation visits returned to in-person assessment by 2022, while 67.0% and 41.4% follow-ups remained virtual in 2022 and 2023, respectively. The year of follow-up, sex, employment and Charlston comorbidity index were associated with virtual follow-up use in the multivariable analysis. DISCUSSION: Virtual care remained a predominant method of GEP-NET patient assessment in the peri-pandemic period. These results highlight an opportunity to improve access to subspecialty neuroendocrine cancer care through the continued use of virtual care.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".