Validation of new, circulating biomarkers for gliomas
Bibliographic record
Abstract
OBJECTIVES: Biomarkers are useful clinical tools but only a handful of them are used routinely for patient care. Despite intense efforts to discover new, clinically useful biomarkers, very few new circulating biomarkers were implemented in clinical practice in the last 40 years. This is mainly due to rather poor clinical performance. Here, our goal was to validate the performance of a group of newly discovered circulating biomarkers for glioma by comparing our data with data from a paper recently published in Science Advances. METHODS: We analyzed our own sets of clinical samples (gliomas (n=30), meningiomas (n=20)) and a different analytical assay (Proximity Extension Assay, OLINK Proteomics) to compare the results of Shen and colleagues. RESULTS: Despite the sophistication of the utilized discovery method by the original investigators, we found that the newly proposed biomarkers for glioma (the best one presumably being SERPINA6) did not perform as originally claimed. CONCLUSIONS: Scientific irreproducibility has been extensively discussed in the literature. A large proportion of newly discovered candidate biomarkers likely represent "false discovery" and significantly contribute to the propagation of irreproducible results between investigators. One of the best ways to assess the value of any new biomarker is by independent and extensive validation. Based on our previous classification of irreproducible results, we believe that this new work likely represents another example of biomarker false discovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".