Verification of a proteomic biomarker panel to diagnose minor stroke and transient ischaemic attack: phase 1 of SpecTRA, a large scale translational study
Bibliographic record
Abstract
OBJECTIVE: To derive a plasma biomarker protein panel from a list of 141 candidate proteins which can differentiate transient ischaemic attack (TIA)/minor stroke from non-cerebrovascular (mimic) conditions in emergency department (ED) settings. DESIGN: Prospective clinical study (#NCT03050099) with up to three timed blood draws no more than 36 h following symptom onset. Plasma samples analysed by multiple reaction monitoring-mass spectrometry (MRM-MS). PARTICIPANTS: Totally 545 participants suspected of TIA enrolled in the EDs of two urban medical centres. OUTCOMES: 90-day, neurologist-adjudicated diagnosis of TIA informed by clinical and radiological investigations. RESULTS: The final protein panel consists of 16 proteins whose patterns show differential abundance between TIA and mimic patients. Nine of the proteins were significant univariate predictors of TIA [odds ratio (95% confidence interval)]: L-selectin [0.726 (0.596-0.883)]; Insulin-like growth factor-binding protein 3 [0.727 (0.594-0.889)]; Coagulation factor X [0.740 (0.603-0.908)]; Serum paraoxonase/lactonase 3 [0.763 (0.630-0.924)]; Thrombospondin-1 [1.313 (1.081-1.595)]; Hyaluronan-binding protein 2 [0.776 (0.637-0.945)]; Heparin cofactor 2 [0.775 (0.634-0.947)]; Apolipoprotein B-100 [1.249 (1.037-1.503)]; and von Willebrand factor [1.256 (1.034-1.527)]. The scientific plausibility of the panel proteins is discussed. CONCLUSIONS: Our panel has the potential to assist ED physicians in distinguishing TIA from mimic patients.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".