IRIS - Time To Treatment Individual Participant Meta-Analysis - Update SAP
Bibliographic record
Abstract
(1) Under “Primary hypothesis and analysis” subheading “primary analysis” it is described that analysis will be carried out with the same adjustment as per IRIS main analysis1. However, the wrong adjustment variables are named thereafter. In the IRIS main analysis adjustments variables were: Citation page 5 of IRIS main SAP1: […] “All analyses will be adjusted for the following prognostic variables: • Age • ASPECTS • Atrial fibrillation • Occlusion location on baseline CTA/MRA • Baseline NIHSS • Pre-stroke mRS score • Time from onset to randomization” […] We corrected the statistical analysis plan accordingly. Time from onset to randomization was not included, because onset-to-expected-IVT times (including time from onset to randomization times) are already implemented in the model as covariates of interest. Changes made (page 5 of SAP 1.0): […] Analyses will be adjusted for age, Alberta Stroke Program Early CT Score, atrial fibrillation, occlusion location on basleline CTA/MRA, baseline NIHSS and pre-stroke mRS score as per IRIS main analysis1. […] (2) Because a few TNK patients are included, the title was changes to “Effect of treatment delay on efficacy and safety of intravenous thrombolysis before thrombectomy: A meta-analysis of individual participant data” (page 1)
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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.009 |
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".