2.20 Statistical modelling of blood biomarkers of sport-related concussion: can we improve estimate precision by using all sample replicates instead of means and >20%CV data exclusion?
Bibliographic record
Abstract
Objective To investigate agreement between plasma biomarker measurements run in duplicate and explore the precision of statistical modelling approaches using all replicate data vs standard of practice sample means and >20%CV data exclusion. Design Cohort study. Setting Canadian high-school and community sport settings. Participants Healthy youth athletes participating in the SHRed Concussions study (n=149, 48% female, ages 11–17). Assessment of Risk Factors Previous concussion (Yes/No), age (years), and sex (M/F). Outcome Measures Plasma GFAP, NF-L, UCH-L1, T-tau, and ptau-181 concentrations (SIMOA;Quanterix) were examined. 95% limits of agreement (95%LOA) between biomarker replicates were assessed using Bland-Altman (B-A) analysis. Three multivariable regression models (α=0.05) were performed to assess estimates of association between independent variables and natural-log (ln) transformed biomarker levels. Model 1 (M1): multi-level model, all replicate data; Model 2 (M2): means of replicates; Model 3 (M3): means of replicates, excluding pairs >20%CV. Main Results B-A showed 95%LOA in GFAP (-17.74;18.2 pg/mL), UCH-L1 (-13.77;14.82 pg/mL), NF-L (-1.871;1.801 pg/mL), ptau-181 (-0.5314;0.5391 pg/mL) and T-tau (ln-T-tau back-transformed ratio 65.27%;150.03%). Biomarker-specific regression β-estimates differed between M1–3 for each biomarker (largest difference in M3 given data exclusion >20%CV). Age was associated with ln-UCH-L1 in M2 (β=0.0485; 95%CI:0.0006,0.0964). Sex was associated with ln-NF-L [(M1:β=0.1940; 95%CI:0.0135,0.3745); (M2:β=0.1945; 95%CI:0.0105,0.3786)] and ln-GFAP [(M1:β=-0.1676; 95%CI:-0.3037,-0.0315); (M2:β=-0.1668; 95%CI:-0.3066,-0.0270) in M1–2. M1 displayed narrowest 95%CIs (highest estimate precision). Conclusions Wide 95%LOA in baseline blood biomarkers suggest the mean of sample duplicates may not reflect true population values. Future pediatric sport-related concussion studies may benefit from statistical modelling incorporating all replicate biomarker data to increase estimate accuracy and precision while considering the importance of age and sex.
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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 | 0.215 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".