Twitterchats as a means to educate patients with CRC and caregivers and stimulate collaborations.
Bibliographic record
Abstract
255 Background: During the COVID-19 pandemic, Twitter has been instrumental in accelerating knowledge dissemination and forging collaborations within the medical community and amongst patient advocates. Tweetchats within Twitter are scheduled conversations on a specific topic. In oncology, Tweetchats have been used by cancer advocates to spread awareness and for patient and caregiver education. A colorectal cancer (CRC) specific tweetchat did not previously exist. This abstract describes the creation, and experiences with a CRC specific tweetchat. Methods: The #CRCTrialsChat tweetchat was created by a patient advocate for colorectal cancer patients, caregivers and clinicians to meet and exchange clinical trial-related information. Two gastrointestinal (GI) medical oncologists and two radiation oncologists were enlisted as moderators. The topic for each session is chosen by the patient advocate, who creates an outline and divides the content, which is designed to last a one hour session. The idea is to create engaging, technical, but easy to understand content. Each moderator then works on the answers to their assigned section, which is edited to fit tweet character limit. Sessions may also have guest moderators with expertise on a specific topic. Through tweeting, moderators answer specific questions that come up during the session and later. Results: To date, we have had four sessions covering the following topics: Clinical trial basics, CRC Updates from ASCO22, ClinicalTrialFinders and BRAF-mutated tumors. The content created has been simple and engaging, the format has functioned smoothly, and the reach of #CRCTrialsChat has been steadily increasing. After the most recent session on BRAF in September 2022, the @CRCTrialsChat has 281 followers, 17K impressions and 14.6K profile visits, a reflection of its excellent content. From a clinician perspective, this is a great format to interact with colleagues, discuss enrolling trials and also become familiar with using Twitter. Conclusions: A CRC clinical trial focused tweetchat is an engaging way to deliver trial-related content to an audience of clinicians, patients and caregivers. The current format appears to be an effective way to create and disseminate information. Future sessions will focus on ctDNA, molecular markers such as KRAS and HER2, and rectal cancer trials. Our hope is that #CRCTrialsChat will stimulate continued patient and clinician engagement, increase awareness of clinical trials, enhance trial participation and initiate patient-centric research and collaborations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".