Perception and acquaintance of stroke specialists on non-inferiority trials: An international survey
Bibliographic record
Abstract
INTRODUCTION: The adoption of non-inferiority trial designs for assessing new interventions in stroke treatment is on the rise. We designed a survey to assess stroke specialists' understanding and familiarity with non-inferiority trials and margins. METHODS: A brief web-based questionnaire was sent to the members of the World Stroke Organization (WSO). The median acceptable non-inferiority margins in different research settings provided by responders were summarized and reported according to the acquaintance of responders with non-inferiority trials. RESULTS: A total of 120 WSO members from 42 countries responded to the survey. Thirty-two percent (32 %) of respondents self-identified as being very familiar with non-inferiority trials, while 6 % identified as extremely familiar. When asked about the impact of non-inferiority trials on improving stroke patient care, 42 % rated it as high and 45 % as moderate. 83 % of responders reported that the findings of non-inferiority trials affect their clinical practice. Ease of administration, relative effect of the standard treatment, clinical implications of inappropriately introducing the new treatment, availability, price, ease of storage and shipping were all considered as factors that should influence the size of the non-inferiority margin. The magnitude and variability of acceptable non-inferiority margins were seen to decrease as the acquaintance of responders with non-inferiority trials increased. CONCLUSION: Although responders acknowledge the importance of non-inferiority trials, most have limited acquaintance with this research design. Educational activities are needed to enhance literacy in non-inferiority trials and the interpretation of non-inferiority margins.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".