Point-of-Care Urine Metabolomics Test to Diagnose Colorectal Cancers in Low- and Middle-Income Countries: A Pilot Controlled Trial in Southwestern Nigeria
Bibliographic record
Abstract
PURPOSE: To test the feasibility of a point-of-care (POC) real-time urine metabolomics test, evaluate its validity in diagnosing colorectal cancer (CRC) among at-risk patients, and assess the willingness of patients in Southwestern Nigeria to use and pay for the test. METHODS: This was a pilot-controlled trial carried out among 72 patients (34 cases and 38 controls) in southwestern Nigeria. The cases were those with histopathological diagnosis of CRC while controls were at-risk adults. The POC biosensor used a disposable chip and can be connected to a smart device using Bluetooth, and reported if the patient's urine contained metabolites consistent with CRC. We assessed validity of the test using sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV), and prespecified a specificity of 50% with a goal of ≥80% sensitivity to estimate the potential of the test to half the referrals to colonoscopy. Additionally, we assessed perception toward the test and willingness to uptake the urine test using structured questionnaires. RESULTS: The overall sensitivity, specificity, PPV, and NPV for all respondents were 91.18%, 81.58%, 81.58%, and 91.18% respectively, with an area under the receiver operating characteristic curve of 0.86. With specificity fixed at 50%, the overall sensitivity for all respondents was 94.5%, and all stratifications had sensitivity >90%. Overall, 70 (98.6%) were satisfied with the urine-based CRC screening, and respondents were willing to pay a mean amount of 8,008.20 Naira (about $5.2 US dollars) for the test. CONCLUSION: Our urine metabolite early diagnosis POC test met our predetermined criteria for success and had high acceptance rates among Nigerian patients, supporting a future multi-institutional implementation trial assessing our ability to scale up utilization.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".