Transcranial Laser Therapy and Infarct Volume
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Two randomized trials suggested that transcranial laser therapy (TLT) may benefit patients with acute ischemic stroke, although efficacy has not been confirmed. Supportive proof of concept could be demonstrated if TLT reduces the volume of cortical infarction. METHODS: The NeuroThera Efficacy and Safety Trial-2 (NEST-2) was a randomized trial of TLT versus sham in patients with acute ischemic stroke treated within 24 hours of onset. Infarct volumes were measured quantitatively and semiquantitatively on all protocol-required computed tomography (or MRI, if clinically indicated) scans performed on day 5 (±2). Two approaches assessed treatment effects on cortex: (1) indirectly, by analyzing total infarct volume among patients with clinical presentations suggesting cortical involvement; and (2) directly, by assessing the cortical Alberta Stroke Program Early CT Score (cASPECTS) components (M1-M6, anterior, posterior) on a 0- to 8-point modified scale. RESULTS: A total of 640 subjects had scans (576 computed tomography, 64 MRI) on day 5. The reliability of ASPECTS (intraclass correlation coefficient=0.85) and cASPECTS (intraclass correlation coefficient=0.82) was excellent, and total ASPECTS was correlated with total infarct volume (r=0.71). In the overall study population, there was no impact of TLT on total infarct volume (P=0.30), total ASPECTS (P=0.85), or cASPECTS (P=0.89). Similarly, no effect was seen in any of the following prespecified subgroups selected to indicate cortical involvement: baseline National Institutes of Health Stroke Scale score >10, Oxfordshire Total Anterior Circulation Syndrome, subjects with aphasia or extinction at baseline, or subjects with radiographic involvement of cortex. CONCLUSIONS: TLT was not associated with a reduction in overall or cortical infarct volume as measured on computed tomography in the subacute phase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".