Common Data Elements Reported in Mechanical Thrombectomy for Acute Ischemic Stroke: A Systematic Review of Active Clinical Trials
Bibliographic record
Abstract
BACKGROUND: New trials are planned regularly to provide the highest quality of evidence and invade new occlusion territories, which requires a pre-defined reporting strategy with consistent, common data elements for more straightforward collective evidence synthesis. We sought to review all active endovascular thrombectomy trials to investigate their patient selection criteria, intervention description, and reported outcomes. METHODS: A literature search was systematically conducted on clinicaltrials.gov for active trials and all intervention, inclusion criteria, and outcomes reported were extracted. A qualitative synthesis of the frequency of study design types and data elements are graphically and narratively presented. RESULTS: A total of 32 studies were tagged and included in the final qualitative analysis. The inclusion criteria were highly variable, including different cut-offs for the last well-known baseline National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, and modified Rankin scale (mRS). Half of the studies (16/32) mentioned "thrombectomy" without defining which technique or device was used, and the final thrombolysis in cerebral infarction scale was provided in 19 (59.4%) studies. Heterogeneity was also present among the studies reporting a first-pass effect, both in how studies defined the outcome and in used ranges for mRS. Mortality and intracerebral hemorrhage (ICH) were more homogenous in their presentation and follow-up. CONCLUSIONS: There is a great degree of heterogeneity in the active thrombectomy trials concerning inclusion criteria, interventions used, and how outcomes are being reported.
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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 | 0.189 | 0.539 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.059 | 0.045 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".