Response to Letter by Gonzalez-Hernandez et al
Bibliographic record
Abstract
We thank Gonzalez-Hernandez et al for their interest in our article. Firstly, we acknowledge that treatments similar to FASTER 2 /EXPRESS were initiated quickly in this population, which may have contributed to the low incidence of recurrence in the first few days. Given that these strategies are frequently implemented acutely this study highlights the growing importance of persistent vessel occlusion with ongoing infarction of penumbral tissue 3 as the critical "untreated" cause for stroke progression. We agree that if all proven secondary prevention treatments are used 4 then the mechanism behind worsening in many cases is from progression of the presenting event, rather than a distinct recurrent event. We also agree that the classic definition of TIA (made at 24 hours) has little use in centers that can assess patients very early into their symptoms. Our work highlights the unstable nature of these patients very early in their disease process. We also found that the presence of minor neurological deficits may further identify patients who are unstable. Further studies are required in larger populations to confirm our findings. Mechanistic information that can be urgently acquired using MRI or CT/CTA is needed in future trials in these patients. This way we can assess which subset of patients benefit from individual treatments and eventually will be able to tailor treatments on an individual patient basis.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.032 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 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".