Abstract WMP20: Intracluster Correlation Coefficients and Reliability of Randomised Multicentre Stroke Trials within VISTA
Bibliographic record
Abstract
Background/Purpose: Reliable estimates of intracluster correlation coefficients (ICCs) for specific outcome measures are crucial for sample size calculations of future cluster randomised trials. ICCs indicate the proportion of data variability that is explained by defined levels of clustering. Methods: ICCs were estimated from linear and generalised linear mixed models using maximum likelihood estimation for common measures used in stroke research, including modified Rankin Skale (mRs), National Institutes of Health Stroke Scale (NIHSS) and Barthel Index (BI). Results: Data were available for 11841 patients with ischaemic stroke from 11 randomised trials. After adjusting for age, thrombolysis, and baseline NIHSS, the median ICC for follow-up data, using centre as the level of clustering, ranged from 0.007 to 0.041. The ICCs for follow-up data using trial, continent or year of enrolment as level of clustering were distinctly lower. Less than 1% of the variability of mRS, NIHSS, and BI was explained by any of these three cluster levels. Conclusion: These estimates of relevant ICCs should assist trial planning. For example the sample size for a cluster trial with 150 patients per centre using ordinal analysis of mRS should be inflated by 2.0 due to the ICC of 0.007; whereas the ICC of 0.031 using mRS dichotomised above mRS 0-1, requires inflation by 5.6. The low contribution of trials, year or continent of enrolment to overall variation in outcome offers reassurance that analyses using pooled data from multiple trials in VISTA are unlikely to suffer from bias from these sources. Table: Adjusted ICCs with various levels of clustering for outcome at 90 days.
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 | 0.318 | 0.712 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".