Time trends for drug specific adverse events in patients on sunitinib; implications for remote monitoring.
Bibliographic record
Abstract
INTRODUCTION: Sunitinib is a multi-targeted receptor tyrosine kinase inhibitor used to treat metastatic renal cell carcinoma (mRCC). Patients on sunitinib do require regular in-person appointments to monitor for adverse events (AEs). Given the Covid-19 pandemic, regular in-person visits expose patients to an increased risk of infection in addition to potentially preventable travel costs. This study investigated the feasibility of implementing a remote monitoring strategy for patients being treated with sunitinib for mRCC by examining the time trends of AEs. MATERIALS AND METHODS: In this retrospective chart review of patients with a diagnosis of mRCC, 167 patients received sunitinib during their treatment. The time between initiation of treatment and the first AE was recorded. The AEs were categorized according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5. Survival analysis was used to calculate the time-to-AE. RESULTS: Of the 167 patients identified, 145 experienced an AE (86.8%). Hypertension was the most common AE with 80% of AEs were ≤ Grade 2. Incidence of AE dropped by 91% after 3 months follow up and a further 36% after 6 months. The cumulative incidence of AEs were 87.8%, 94.6% and 98.0%, at 3, 6 and 9 months respectively. The severity of AEs observed were 39.3%, 38.6%, 20.7%, 1.4%,0% of Grade 1-5 events respectively. A trend of grade migration to less severe grades was also shown over time, with percentage of Grade ≥ 3 toxicity dropping from 22% between 0-3 months to 14% beyond 6 months follow up. CONCLUSIONS: The role of remote monitoring for mRCC patients on sunitinib remains relevant now with new waves of the Covid-19 pandemic, triggered by novel variants. The majority of AEs observed were of low severity ≤ Grade 2, with a trend of reduced AE frequency and severity most prevalent beyond 3 months of follow up. This data appears to support the implementation of a remote monitoring strategy 3 months after initiation of treatment.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".