Evaluation of alpha-II-spectrin breakdown products as potential biomarkers for early recognition and severity of aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
This study assessed whether cytoskeletal protein alpha-II spectrin breakdown products (SBDP150, SBDP145, and SBDP120) would identify the presence of aSAH and be associated with severity (GCS score, WFNS grade and survival to hospital discharge). This prospective case-control study, conducted at a tertiary care Level I trauma center, enrolled adult patients with angiography confirmed aSAH who underwent ventriculostomy placement for cerebrospinal fluid (CSF) drainage. There were 40 patients enrolled in the study, 20 with aSAH and 20 control subjects. Patients with aSAH were a mean age of 54 (SD15) and 75% were female. There were significant differences in SBDP150, SBDP145, and SBDP120 CSF levels between patients with and without aSAH (p < 0.001), even in those presenting with a GCS Score of 15 and a WFNS Grade 1. The AUC for distinguishing aSAH from control subjects was 1.0 for SBDP150 and SBDP145, and 0.95 for SBDP120. SBDP150 and SBDP145 both yielded sensitivities and specificities of 100% and SBDP120 was 90% and 100% respectively. Moreover, there were significantly higher levels of SBDP150 and SBDP145 in the non-survivors than in the survivors (p < 0.001). This study demonstrates the potential that SBDP's have as biomarkers for recognition and severity of aSAH. A larger prospective study is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".