Validation of new, circulating biomarkers for glioma
Bibliographic record
Abstract
Abstract 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 poor clinical performance. Here, our goal was to validate a group of newly discovered circulating biomarkers for glioma by comparing our data with data from a paper recently published in Science Advances. We analyzed our own sets of clinical samples and a different analytical assay to compare the results of Shen and colleagues. Despite the sophistication of the utilized discovery method, we found that the newly proposed biomarkers for glioma (such as SERPINA6) did not perform as expected. Scientific irreproducibility has been extensively discussed in the literature. A large proportion of newly discovered candidate biomarkers likely represent “false discovery” and contribute to irreproducible results. The best way to assess the value of any new biomarker is by independent and extensive validation. Based on our previous classification, we believe that this work represents another example of a false discovery.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".