Bibliographic record
Abstract
We read with interest the multicenter manuscript of Psychogios et al. in which they reported on the comparison of infarct core and tissue at risk maps generated by four different vendors as well as visual Cerebral Blood Volume-Alberta Stroke Program Early CT Score (CBV-ASPECTS) and visually assessed collateral scores [1].They related the maps of 182 patients undergoing mechanical thrombectomy (MT) and receiving a TICI 2b, 2c or III reperfusion to the clinical outcome assessed with the modified Rankin score (mRS) and the functional disability defined as mRS > 2. They calculated mean differences between RAPID (iSchemaView Inc, Menlo Parc, CA, USA) and other software packages and illustrated them with Bland-Altman plots.They concluded that the infarct core defined by the RAPID software correlates best with the clinical outcome whilst VEOcore (VEObrain GmbH, Freiburg, Germany) and syngo.via(Siemens Healthineers AG, Erlangen, Germany) overestimate the infarct core and Olea (OLEA medical Inc., La Ciotat, France) underestimates it [1].The message is clear but can we trust it?In the manuscript the authors clearly state that out of 215 cases 33 cases have been excluded from the final analysis due to "... technical failure of at least 1 perfusion software"; however, if we take a look at the Bland-Altman plot of RAPID-VEOcore (only available in the Supplemental Material) there is a striking outlier in the infarct core volume difference of around -2131 mL (which is distinctly larger than an entire brain).This outlier leads to a massive bias in the statistics: it can be estimated that without the outlier the true mean difference between RAPID and VEOcore is in a very good agreement range of -1.5 mL instead of the -13.4 mL reported in the manuscript.
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.210 | 0.215 |
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".