Vendor-neutral sequences (VENUS) and multicenter reproducibility of qMRI at ISMRM 2022
Bibliographic record
Abstract
Our hypothesis is the title itself: Vendor-neutral sequences (VENUS) improve multi-center reproducibility of quantitative MRI. For an interactive, online executable and fully reproducible exploration of our results you can simply visit https://qmrlab.org/VENUS! In this jupyter book, you can find all the open-source resources including GitHub and data repositories. The journal article is published in MRM: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.29292 Here, I share 3 pptx files, all presented at the ISMRM 2022 in London, UK. Each presentation is an award winner: 🎖 7124-karakuzu.pptx - Summa Cum Laude 🥇 karakuzu_wmsg.pptx - White Matter Study Group Research Award (1st place) 🥇 karakuzu_rrsg.pptx - Reproducible Research Study Group Research Award (1st place)
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".