Avoiding false discovery in biomarker research
Bibliographic record
Abstract
BACKGROUND: Human tyrosine-protein phosphatase non-receptor type substrate 1α (SIRPA) is a surface marker identified in cardiomyocytes differentiated from human embryonic stem cells. Our objective was to determine if circulating SIRPA levels can serve as a biomarker of cardiac injury in children undergoing open heart surgery. RESULTS: Paired pre- and post-operative serum samples from 48 pediatric patients undergoing open heart surgery and from 6 pediatric patients undergoing non-cardiac surgery (controls) were tested for SIRPA protein levels using commercially available SIRPA ELISA kits from two manufacturers. Post-operative SIRPA concentrations were significantly higher in patients after cardiac surgery compared to non-cardiac surgery when tested using SIRPA ELISA kits from both manufacturers. To verify the identity of the protein detected, recombinant human SIRPA protein (rhSIRPA) was tested on both ELISA kits. The calibrator from both ELISA kits was analyzed by Western blot as well as by Mass Spectrometry (MS). Western blot analysis of calibrators from both kits did not identity SIRPA. MS analysis of calibrators from both ELISA kits identified several inflammatory markers and albumin but no SIRPA was detected. CONCLUSIONS: We conclude that commercially available ELISA kits for SIRPA give false-positive results. Verifying protein identity using robust protein characterization is critical to avoid false biomarker discovery when using commercial ELISA kits.
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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 | 0.378 | 0.583 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".