The SENSOR System: Using Standardized Data Entry and Dashboards for Review of Scientific Studies on the Utility of Blood-Based Protein Biomarkers for Patients with Mild Brain Injury
Bibliographic record
Abstract
Abstract Background There is no objective way of diagnosing or prognosticating acute traumatic brain injuries (TBIs). A systematic review conducted by Mondello et al . reviewed studies looking at blood based protein biomarkers in the context of acute mild traumatic brain injuries and correlation to results of computed tomography scanning. This paper provides a summary of this same literature using the SENSOR system. Methods An existing review written by Mondello et al . was selected to apply the previously described SENSOR system (Kamal et al.) that uses a systematic process made up of a Google Form for data intake, Google Drive for article access, and Google Sheets for the creation of the dashboard. The dashboard consisted of a map, bubble graphs, multiple score charts, and a pivot table to facilitate the presentation of data. Results A total of 29 entries were inputted by two team members. Sensitivities, specificities, positive predictive values (PPVs), negative predictive values (NPVs), demographics, cut-off levels, biomarker levels, and assay ranges were analyzed and presented in this study. S100B and GFAP biomarkers may provide good clinical utility, whereas UCH-L1, C-Tau, and NSE do not. Discussion This study determined the feasibility and reliability of multiple biomarkers (S100B, UCH-L1, GFAP, C-tau, and NSE) in predicting traumatic brain lesions on CT scans, in mTBI patients, using the SENSOR system. Many potential limitations exist for the existing literature including controlling for known confounders for mild traumatic brain injuries. Conclusion The SENSOR system is an adaptable, dynamic, and graphical display of scientific studies that has many benefits, which may still require further validation. Certain protein biomarkers may be helpful in deciding which patients with mTBIs require CT scans, but impact on prognosis is still not clear based on the available literature.
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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 | 0.098 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.038 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".