To Do or Not to Do; Dilemma of Intra-Arterial Revascularization in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: There has still been lack of evidence for definite imaging criteria of intra-arterial revascularization (IAR). Therefore, IAR selection is left largely to individual clinicians. In this study, we sought to investigate the overall agreement of IAR selection among different stroke clinicians and factors associated with good agreement of IAR selection. METHODS: From the prospectively registered data base of a tertiary hospital, we identified consecutive patients with acute ischemic stroke. IAR selection based on the provided magnetic resonance imaging (MRI) results and clinical information were independently performed by 5 independent stroke physicians currently working at 4 different university hospitals. MRI results were also reviewed by 2 independent experienced neurologists blinded to clinical data and physicians' IAR selection. The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was calculated on initial DWI and MTT. We arbitrarily used ASPECTS differences between DWI and MTT (D-M ASPECTS) to quantitatively evaluate mismatch. RESULTS: The overall interobserver agreement of IAR selection was fair (kappa = 0.398). In patients with DWI-ASPECTS >6, interobserver agreement was moderate to substantial (0.398-0.620). In patients with D-M ASPECTS >4, interobserver agreement was moderate to almost perfect (0.532-1.000). Patients with higher DWI or D-M ASPECTS had better agreement of IAR selection. CONCLUSION: Our study showed that DWI-ASPSECTS >6 and D-M ASPECTS >4 had moderate to substantial agreement of IAR selection among different stroke physicians. However, there is still poor agreement as to whether IAR should not be performed in patients with lower DWI and D-M ASPECTS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".