Understanding physician preferences about combined thrombolysis and thrombectomy in patients with large vessel occlusion: An international cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: A recently published individual participant-level meta-analysis found that EVT alone was not non-inferior to combined intravenous thrombolysis (IVT) and EVT. Our aim was to determine factors that influence physicians' treatment choice of IVT-alone versus EVT-alone versus a combined approach. METHODS: We performed an international, structured, invite-only survey among physicians treating patients presenting with AIS. Respondents were asked 16 multiple choice questions. Fourteen questions involved the respondent being provided with a clinical scenario. In each scenario, a patient was presenting with an AIS with LVO, varying a single clinical or imaging feature. RESULTS: A total of 282 stroke physicians (mean age 46 years, 75 % males) participated in the survey. In LVO stroke, eligible for both IVT and EVT, without other qualifiers, 220 (85.9 %) respondents chose to pursue a combined approach. For age over 80 years, 191 (74 %) participants opted for combined approach, which decreased to 121 (48.2 %) with dementia and 148 (57.4 %) if the patient was on dual anti-platelet therapy (DAPT). Of respondents choosing combination therapy in a patient above the age of 80, only 105 (56.8 %) would pursue the same in a patient with dementia. For imaging factors, 177 (72.8 %) opted for a combined approach for intracranial carotid occlusion, which decreased to 160 (65.3 %) in tandem occlusions. Overall, 88 (38 %) respondents agreed to the statement "I am uncomfortable with uncertainty in patient care". CONCLUSIONS: In a typical patient with AIS due to LVO, most respondents still choose a combined revascularization approach but discrepancy in decision-making increases in complex scenarios.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".