Abstract 5523: Impact of COVID-19 on longitudinal breast cancer research studies involving TNBC patients
Bibliographic record
Abstract
Abstract Background Triple-negative breast cancer (TNBC) accounts for ~15% of breast cancer diagnoses but is linked to worse outcomes and comprises a disproportionate number of breast cancer deaths. The TNBC pilot study is a prospective longitudinal study to provide a critical resource for understanding TNBC disease. However, the pandemic impacted the collection of samples. Objective To highlight the impacts of COVID-19 on this longitudinal cancer translational research study including the patient's perspective and to develop recommendations to avoid future disruptions. Methods 389 participants were enrolled in the prospective longitudinal cohort, which collected serial blood samples for up to 5 years. Due to the pandemic, research was curtailed for 6 months due to concerns about patient safety, halting the collection of blood samples. Missed samples and data gaps were documented. To complement this, we initiated a survey capturing the patient perspective on their experience of the study disruption due to COVID. Results 217 enrolled participants missed a blood draw or had a collection outside the study window. 158 patients missed 1 time-point collection, and 59 patients missed ≥ 2 collections. Of the 217 participants who missed a collection, 6 disease recurrence diagnoses and 3 deaths occurred during research curtailment. The collection of survey responses from participants is ongoing and will be presented at the AACR Annual Meeting. Conclusion Missed samples resulted in irreplaceable data gaps critical to monitoring patient outcomes, and reduced cohort sampling during the pandemic. Our current knowledge of the risks suggests that with proper informed consent, collections could have continued. To mitigate disruption in future clinical studies, clear plans should be part of study design to provide continuity. The participants’ experience to be reported will also help researchers understand their issues and help develop policies. Missed collections and clinical events during COVID-19 research curtailment Number of patients Total with missed or late blood draws 217 Missed 1 collection 158 Missed ≥ 2 collections 59 Diagnosed with disease recurrence 6 Deaths 3 Citation Format: Cherie Bates, Esther Kong, Elena Zaikova, Samuel Aparicio, Karen Gelmon. Impact of COVID-19 on longitudinal breast cancer research studies involving TNBC patients. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5523.
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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 | 0.295 | 0.500 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".