Statistical principles in neurointervention part 2. Multivariable analysis: generalized linear models, modification, confounding, and mediation
Bibliographic record
Abstract
This two part series on statistical principles in neurointervention offers a comprehensive foundation for neurointerventionalists to engage with both fundamental and advanced statistical principles. This series aims to equip neurointerventionalists with essential statistical knowledge for critically reviewing literature and conducting methodologically sound research. Part one of this series covered fundamental concepts such as frequentism, study types, data types, summarization, visualization, hypothesis testing, and univariable analysis. This review is the second part of the series and covers advanced statistical concepts such as inference versus prediction, multivariable analysis, choice of covariates, confounding, mediation, modification, and generalized linear models. Together, these papers create a cohesive framework, allowing practitioners to critically evaluate research and apply rigorous statistical methods to their own studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads 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".