Evaluation of the analytical performance of the novel NS-Prime system and examination of temperature stability of fecal transferrin compared with fecal hemoglobin as biomarkers in a colon cancer screening program
Bibliographic record
Abstract
OBJECTIVE: To examine the analytical aspects of fecal transferrin (Tf) and hemoglobin (Hb) measured on the NS-Prime analyzer for use in a colon cancer screening program. DESIGNS AND METHODS: Method evaluation and temperature stability studies for fecal Tf and Hb were completed. A method comparison was carried out against the NS-Plus system using samples collected from 254 screening program participants. A further 200 samples were analyzed to help determine suitable reference limits for fecal Tf using these systems. RESULTS: The assay for fecal Tf showed acceptable linearity, precision, and recovery, and showed minimal carryover with low potential for impact by the prozone effect. The 95th percentile for fecal Tf obtained for the reference population was 4.9 µg/g feces. The collection device sufficiently maintained fecal Tf and Hb stability for at least 7 days at room temperature, 4 °C, and -20 °C. Fecal Tf and Hb were most stable at 4 °C and -20 °C, but showed considerable loss (20-40%) of both proteins at 37 °C within the first 7 days. Mixing small amounts of blood into diluted fecal samples maintained at 37 °C for various time periods showed >50% loss of both proteins within 1 h of incubation. CONCLUSIONS: The NS-Prime analyzer showed acceptable performance for fecal Tf and Hb. These studies suggest that use of both Tf and Hb together as biomarkers will result in higher positivity rates, but this may not be attributed to greater stability of Tf over Hb in human feces.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".