Exploring Protein Quantitation Methods to Detect Useful Plasma Biomarkers for Distinguishing Ischemic Stroke from Mimic (I2.013)
Bibliographic record
Abstract
Objective: To prospectively compare plasma proteins between disabling ACVS and mimic patients using two protein quantitation methods, as part of a larger multi-phase proteomic ACVS study. Background: A blood-based biomarker test for acute cerebrovascular syndrome (ACVS) would help select patients for advanced imaging and specialist referral, particularly in mild disease where mimic rates are over 50[percnt]. Methods: Blood was collected in the ED from ACVS patients (n=20) with an NIHSS>5 and <24 hours from symptom onset. Mimic patients (n=20) were enrolled from an ambulatory TIA clinic. Patient phenotype was based on clinical findings and imaging. Average time from stroke symptom onset to blood collection was < 10 hrs. 95[percnt] of EDTA plasma samples were frozen within 41 minutes of collection. 31 proteins were quantified using commercial ELISA kits and 141 proteins were analyzed using mass spectrometry (1D-LC/MRM). Results: 60[percnt] of stroke and 65[percnt] of mimic patients were female, and stroke patients were older: median [range] age 77 [46, 95] vs. 63[36, 77]. The 3 most frequent mimic sub-types were migraine (25[percnt]), neuropathy(20[percnt]), and transient global amnesia (20[percnt]). 29 proteins (23 by MRM, 6 by ELISA) were differentially abundant between mimic and stroke (each ELISA p<0.1, MRM p<0.05). Including the first two principal components of these proteins in a logistic regression model significantly improved discrimination (p<0.01, AUC 0.985 vs. 0.810) over a model with age. The 29 significant proteins included markers of inflammation (45[percnt]), coagulation (41[percnt]), neurovascular unit injury (7[percnt]), and atrial fibrillation (7[percnt]). Conclusions: These results provide proof of concept that proteomic signals can be useful in ACVS diagnosis in more severe cases.The analyses have shaped our prospective verification (completed, n=560) and validation studies (ongoing, n=1200) of the use of proteomics in the diagnosis of mimic vs. mild-ACVS in early Emergency Department triage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".