Abstract PR017: Enabling broad population access to facilitate gut microbiome discoveries
Bibliographic record
Abstract
Abstract Microbial associations with colorectal cancer (CRC) are well recognized as well as the potential of using microbiome-based signals for broad-scale, non-invasive screening programs, particularly in underserved communities. Variability within microbiome workflows including sample collection is a contributing factor to inconsistent and non-translatable microbiome results. This limits microbiome discoveries associated with CRC and the development of such screening programs. In addition, ensuring participant and patient adherence can be a challenging prospect with fecal sample collections, and non-adherence (i.e., patient dropout, collection errors) can increasingly strain resources. An intuitively designed sample collection device should increase adherence across the general population while minimizing failed collections. Enabling the at-home collection of gut microbiome samples removes barriers between patients and testing while providing the scalability and flexibility needed for large studies or screening programs. Across two studies, 152 naïve participants were recruited to evaluate comprehension of and adherence to the OMNIgene™•GUT Dx device Instructions for Use (IFU). Both studies were designed with key considerations related to user group selection, sample size, population demographics, education level, and relevant real-world scenarios. In the first study (n=118), each participant was observed collecting one simulated “fecal” sample with representation across the Bristol scale (Type 1 to 7). The study observer conducted a follow-up interview with the participant regarding incorrect actions during collection. In the second study (n=34), participants collected one real fecal sample at home and returned it to the laboratory using standard postal services. Samples were inspected at the laboratory for collection adherence with the IFU by evaluating device closure, sufficient mixing, and DNA yield. DNA was extracted using the QIAamp PowerFecal Pro kit (QIAGEN) and quantified using PicoGreen (Invitrogen). In both studies, participants were asked to complete comprehension questionnaires to test understanding of the IFU used during collection. Participants demonstrated high rates of comprehension by answering the questions correctly 94% of the time. Participants observed collecting simulated samples demonstrated success rates of 87% for all tasks overall. Samples evaluated from the second study demonstrated participant adherence where 100% of samples were correctly returned via standard postal systems supporting at-home direct-to-consumer settings; 100% of participants properly closed the tube ensuring no loss of sample during transport; 97% of participants adequately mixed the sample; and 97% of participant samples provided sufficient DNA (≥ 120 ng) per extraction aliquot for downstream analysis. Results from these usability studies demonstrate that the OMNIgene™•GUT Dx device provides an easy-to-use at-home collection solution for diverse populations, especially at-risk, minority populations without easy access to clinical services. Citation Format: Ashlee Brown, Savannah Colameco, Tara Crawford Parks. Enabling broad population access to facilitate gut microbiome discoveries [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR017.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".