Abstract A022: Exploring the association of quantitative fecal immunochemical test (qFIT) with red-flag symptoms of early onset colorectal cancer
Bibliographic record
Abstract
Abstract Background: Quantitative fecal immunochemical test (qFIT) results, available as continuous numeric values, have been used as an effective triage mechanism for patients with symptoms of CRC to assess the need for follow-up colonoscopy in European countries. Because early-onset CRC (EOCRC) is primarily diagnosed after young patients report non-specific and vague red-flag symptoms, we aim to explore the association of numeric qFIT values with red-flag symptoms of EOCRC in an American healthcare system. Methods: We used electronic health records (EHR) data for patients aged 18-49 reporting >=1 red-flag symptom and having a subsequent qFIT (Polymedco) test value between August 2019- June 2021. Red-flag symptoms were assessed in the 2 years prior to the last qFIT and could include an EHR visit diagnosis code for at least one of: abdominal pain, rectal bleeding, rectal pain, weight loss, diarrhea, or constipation. We used log-transformed multivariable linear regression to assess the association of the number of symptoms and type of symptom with qFIT values. Results: We analyzed EHR data for 536 patients with a qFIT value between Aug 2019-Jun 2021. The average age was 46.1 years, with majority being women (73%). Approximately 56% of the patients were non-Hispanic White, 20% were non-Hispanic Black, and 21% were Hispanic. Most (53%) patients had BMI>30 kg/m2. The median qFIT value was 4.5 ng/ml, and the patients reported a median of two symptoms in the 2 years prior to their last qFIT. Reported symptoms of rectal bleeding and rectal pain had odds of higher qFIT values. Conclusion: Our exploratory study in 536 safety-net patients revealed that rectal bleeding and rectal pain had odds of higher qFIT values. This points to the importance of identifying these symptoms and initiating EOCRC testing in these patients. Future studies should evaluate the association of these qFIT values with EOCRC diagnoses. Citation Format: Amy E. Hughes, Rasmi G. Nair, Cynthia Ortiz, Wyley Jay. Gates. Exploring the association of quantitative fecal immunochemical test (qFIT) with red-flag symptoms of early onset colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A022.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".